Museums have recognized the need of supporting visitors in fulfilling a personalized experience when visiting artwork collections and have started to adopt recommender systems as a way to meet this requirement. Content-based recommender systems analyze features of artworks previously rated by a visitor and build a visitor model or profile, in which preferences and interests are stored, based on those features. For example, the profile of a visitor might store the names of his or her favorite painters or painting techniques, extracted from short textual descriptions associated with artworks. The user profile is then matched against the attributes of new items in order to provide personalized suggestions. The Web 2.0 (r)evolution has changed the game for personalization from ‘elitist’ Web 1.0, written by few and read by many, to web content generated by everyone (user-generated content - UGC). One of the forms of UGC that has drawn most attention of the research community is folksonomy, a taxonomy generated by users who collaboratively annotate and categorize resources of interests with freely chosen keywords called tags. In this work, we investigate the problem of deciding whether folksonomies might be a valuable source of information about user interests in the context of recommending digital artworks. We present FIRSt (Folksonomy-based Item Recommender syStem), a content-based recommender system which integrates UGC through social tagging in a classic content-based model, letting users express their preferences for items by entering a numerical rating as well as by annotating items with free tags. Experiments show that the accuracy of recommendations increases when tags are exploited in the recommendation process to enrich user profiles, provided that tags are not used as a surrogate of the item descriptions, but in conjunction with them. FIRSt has been developed within the CHAT project “Cultural Heritage fruition & e-Learning applications of new Advanced (multimodal) Technologies” – and it is the core of a bouquet of web services designed for personalized museum tours.

Wealth Management is a business model operated by banks and brokers, that offers a broad range of investment services to individual clients, in order to help them reach their investment objectives. Wealth management services include investment advisory, subscription of mandates, sales of financial products, collection of investment orders by clients. Due to the complexity of the task, which largely requires a deep knowledge of the financial domain, a recend trend in the area is to exploit recommendation technologies to support financial advisors and to improve the effectiveness of the process. This paper proposes a framework to support financial advisors in the task of providing clients with personalized investment strategies. Our methodology is based on the exploitation of case-based reasoning. A prototype version of the platform has been adopted to generate personalized portfolios, and the performance of the framework shows that the yield obtained by recommended portfolios overcomes that of portfolios proposed by human advisors in most experimental settings.

This work presents a virtual player for the quiz game “Who Wants to Be a Millionaire?”. The virtual player demands linguistic and common sense knowledge and adopts state-of-the-art Natural Language Processing and Question Answering technologies to answer the questions. Wikipedia articles and DBpedia triples are used as knowledge sources and the answers are ranked according to several lexical, syntactic and semantic criteria. Preliminary experiments carried out on the Italian version of the boardgame proves that the virtual player is able to challenge human players.

This paper provides an overview of the work done in the Linked Open Data-enabled Recommender Systems challenge, in which we proposed an ensemble of algorithms based on popularity, Vector Space Model, Random Forests, Logistic Regression, and PageRank, running on a diverse set of semantic features. We ranked 1st in the top-N recommendation task, and 3rd in the tasks of rating prediciton and diversity.

Recommender systems are filters which suggest items or information that might be interesting to users. These systems analyze the past behavior of a user, build her profile that stores information about her interests, and exploit that profile to find potentially interesting items. The main limitation of this approach is that it may provide accurate but likely obvious suggestions, since recommended items are similar to those the user already knows. In this paper we investigate this issue, known as overspecialization or serendipity problem, by proposing a strategy that fosters the suggestion of surprisingly interesting items the user might not have otherwise discovered. The proposed strategy enriches a graph-based recommendation algorithm with background knowledge that allows the system to deeply understand the items it deals with. The hypothesis is that the infused knowledge could help to discover hidden correlations among items that go beyond simple feature similarity and therefore promote non obvious suggestions. Two evaluations are performed to validate this hypothesis: an in-vitro experiment on a subset of the hetrec2011-movielens-2k dataset, and a preliminary user study. Those evaluations show that the proposed strategy actually promotes non obvious suggestions, by narrowing the accuracy loss.

Thanks to the continuous growth of collaborative platforms like YouTube, Flickr and Delicious, we are recently witnessing to a rapid evolution of web dynamics towards a more 'social' vision, called Web 2.0. In this context collaborative tagging systems are rapidly emerging as one of the most promising tools. However, as tags are handled in a simply syntactical way, collaborative tagging systems suffer of typical Information Retrieval (IR) problems like polysemy and synonymy: so, in order to reduce the impact of these drawbacks and to aid at the same time the so-called tag convergence, systems that assist the user in the task of tagging are required. In this paper we present a system, called STaR, that implements an IR-based approach for tag recommendation. Our approach, mainly based on the exploitation of a state-of-the-art IR-model called BM25, relies on two assumptions: firstly, if two or more resources share some common patterns (e.g. the same features in the textual description), we can exploit this information supposing that they could be annotated with similar tags. Furthermore, since each user has a typical manner to label resources, a tag recommender might exploit this information to weigh more the tags she already used to annotate similar resources. We also present an experimental evaluation, carried out using a large dataset gathered from Bibsonomy.

The chapter presents the SWAPTeam participation at the ECML/PKDD 2011 - Discovery Challenge for the task on the cold start problem focused on making recommendations for new video lectures. The developed solution uses a content-based approach because it is less sensitive to the cold start problem that is commonly associated with pure collaborative filtering recommenders. The Challenge organizers encouraged solutions that can actually affect VideoLecture.net, thus the proposed integration strategy is the hybridization by switching. In addition, the surrounding idea for the proposed solution is that providing recommendations about cold items remains a chancy task, thus a computational resource curtailment for such task is a reasonable strategy to control performance trade-off of a day-to-day running system. The main contribution concerns about the compromise between recommendation accuracy and scalability performance of proposed approach.

The effectiveness of content-based recommendation strategies tremendously depends on the representation formalism adopted to model both items and user profiles. As a consequence, techniques for semantic content representation emerged thanks to their ability to filter out the noise and to face with the issues typical of keyword-based representations. This article presents Contextual eVSM (C-eVSM), a content-based context-aware recommendation framework that adopts a novel semantic representation based on distributional models and entity linking techniques. Our strategy is based on two insights: first, entity linking can identify the most relevant concepts mentioned in the text and can easily map them with structured information sources, easily triggering some inference and reasoning on user preferences, while distributional models can provide a lightweight semantics representation based on term co-occurrences that can bring out latent relationships between concepts by just analying their usage patterns in large corpora of data. The resulting framework is fully domain-independent and shows better performance than state-of-the-art algorithms in several experimental settings, confirming the validity of content-based approaches and paving the way for several future research directions.

In this paper we deal with the problem of providing users with cross-language recommendations by comparing two dierent content- based techniques: the rst one relies on a knowledge-based word sense disambiguation algorithm that uses MultiWordNet as sense inventory, while the latter is based on the so-called distributional hypothesis and exploits a dimensionality reduction technique called Random Indexing in order to build language-independent user proles.

The rapid growth of the so-called Web 2.0 has changed the surfers’ behavior. A new democratic vision emerged, in which users can actively contribute to the evolution of the Web by producing new content or enriching the existing one with user generated metadata. In this context the use of tags, keywords freely chosen by users for describing and organizing resources, spread as a model for browsing and retrieving web contents. The success of that collaborative model is justified by two factors: firstly, information is organized in a way that closely reflects the users’ mental model; secondly, the absence of a controlled vocabulary reduces the users’ learning curve and allows the use of evolving vocabularies. Since tags are handled in a purely syntactical way, annotations provided by users generate a very sparse and noisy tag space that limits the effectiveness for complex tasks. Consequently, tag recommenders, with their ability of providing users with the most suitable tags for the resources to be annotated, recently emerged as a way of speeding up the process of tag convergence. The contribution of this work is a tag recommender system implementing both a collaborative and a content-based recommendation technique. The former exploits the user and community tagging behavior for producing recommendations, while the latter exploits some heuristics to extract tags directly from the textual content of resources. Results of experiments carried out on a dataset gathered from Bibsonomy show that hybrid recommendation strategies can outperform single ones and the way of combining them matters for obtaining more accurate results.

This paper provides an overview of the work done in the ESWC Linked Open Data-enabled Recommender Systems challenge, in which we proposed an ensemble of algorithms based on popularity, Vector Space Model, Random Forests, Logistic Regression, and PageRank, running on a diverse set of semantic features. We ranked 1st in the top-N recommendation task, and 3rd in the tasks of rating prediction and diversity.

In several domains contextual information plays a key role in the recommendation task, since factors such as user location, time of the day, user mood, weather, etc., clearly affect user perception for a particular item. However, traditional recommendation approaches do not take into account contextual information, and this can limit the goodness of the suggestions. In this paper we extend the enhanced Vector Space Model (eVSM) framework in order to model contextual information as well. Specifically, we propose two different context-aware approaches: in the first one we adapt the microprofiling technique, already evaluated in collaborative filtering, to content-based recommendations. Next, we define a contextual modeling technique based on distributional semantics: it builds a context-aware user profile that merges user preferences with a semantic vector space representation of the context itself. In the experimental evaluation we carried out an extensive series of tests in order to determine the best-performing configuration among the proposed ones. We also evaluated Contextual eVSM against a state of the art dataset, and it emerged that our framework overcomes all the baselines in most of the experimental settings.

The exponential growth of the Web is the most influential factor that contributes to the increasing importance of text retrieval and filtering systems. Anyway, since information exists in many languages, users could also consider as relevant documents written in different languages from the one the query is formulated in. In this context, an emerging requirement is to sift through the increasing flood of multilingual text: this poses a renewed challenge for designing effective multilingual Information Filtering systems. How could we represent user information needs or user preferences in a language-independent way? In this paper, we compared two content-based techniques able to provide users with cross-language recommendations: the first one relies on a knowledge-based word sense disambiguation technique that uses MultiWordNet as sense inventory, while the latter is based on a dimensionality reduction technique called Random Indexing and exploits the so-called distributional hypothesis in order to build language-independent user profiles. Since the experiments conducted in a movie recommendation scenario show the effectiveness of both approaches, we tried also to underline strenghts and weaknesses of each approach in order to identify scenarios in which a specific technique fits better.

The exponential growth of the Web is the most influential factor that contributes to the increasing importance of cross-lingual text retrieval and filtering systems. Indeed, relevant information exists in different languages, thus users need to find documents in languages different from the one the query is formulated in. In this context, an emerging requirement is to sift through the increasing flood of multilingual text: this poses a renewed challenge for designing effective multilingual Information Filtering systems. Content-based filtering systems adapt their behavior to individual users by learning their preferences from documents that were already deemed relevant. The learning process aims to construct a profile of the user that can be later exploited in selecting/recommending relevant items. User profiles are generally represented using keywords in a specific language. For example, if a user likes movies whose plots are written in Italian, content-based filtering algorithms will learn a profile for that user which contains Italian words, thus movies whose plots are written in English will be not recommended, although they might be definitely interesting. In this paper, we propose a language-independent content-based recommender system, called MARS (MultilAnguage Recommender System), that builds cross-language user profiles, by shifting the traditional text representation based on keywords, to a more advanced language-independent representation based on word meanings. The proposed strategy relies on a knowledge-based word sense disambiguation technique that exploits MultiWordNet as sense inventory. As a consequence, content-based user profiles become language-independent and can be exploited for recommending items represented in a language different from the one used in the content-based user profile. Experiments conducted in a movie recommendation scenario show the effectiveness of the approach.

Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user [1]. They exploit adaptive and intelligent systems technologies and have already proved to be valuable for coping with the information overload problem in several application domains. However, while most of the previous research has focused on recommendation techniques and algorithms, i.e., how to compute precise and accurate recommendations, only few studies have stood from users' angles to consider the processes and issues related to the actual acceptance of the recommendations. Hence, characterizing and evaluating the quality of users' experience and their subjective attitudes toward the recommendations and the recommendation technologies is an important issue that merits the attention of researchers and practitioners. These issues are important and should be studied both by web technology experts and in the human factor field. The main goal of the first workshop on Decision Making and Recommendation Acceptance issues in Recommender Systems (DEMRA) held at UMAP 2011 was to stimulate the discussion around problems, challenges and research directions about the acceptance of recommendation technologies [2].

Users interact with recommender systems to obtain useful information about products or services that may be of interest for them. But, while users are interacting with a recommender system to fulfill a primary task, which is usually the selection of one or more items, they are facing several other decision problems. For instance, they may be requested to select specific feature values (e.g., camera’s size, zoom) as criteria for a search, or they could have to identify features to be used in a critiquing based recommendation session, or they may need to select a repair proposal for inconsistent user preferences when interacting with a recommender. In all these scenarios, and in many others, users of recommender systems are facing decision tasks. The complexity of decision tasks, limited cognitive resources of users, and the tendency to keep the overall decision effort as low as possible is modeled by theories that conjecture “bounded rationality”, i.e., users are exploiting decision heuristics rather than trying to take an optimal. Furthermore, preferences of users will likely change throughout a recommendation session, i.e., preferences are constructed in a specific decision context and users may not fully know their preferences beforehand. Within the scope of a decision process, preferences are strongly influenced by the goals of the customer, existing cognitive constraints, and the personal experience of the customer. Due to the fact that users do not have stable preferences, the interaction mechanisms provided by a recommender system and the information shown to a user can have an enormous impact on the outcome of a decision process. Theories from decision psychology and cognitive psychology have already elaborated a number of methodological tools for explaining and predicting the user behavior in these scenarios. The major goal of this workshop is to establish a platform for industry and academia to present and discuss new ideas and research results that are related to the topic of human decision making in recommender systems. The workshop consists of a mix of six presentations of papers in which results of ongoing research as reported in these proceedings are presented and two invited talks: Bart Knijnenburg presenting “Simplifying privacy decisions: towards interactive and adaptive solutions” and Jill Freyne and Shlomo Berkovsky presenting: “Food Recommendations: Biases that Underpin Ratings”. The workshop is closed by a final discussion session.

This paper presents the preliminary results of a joint research project about Smart Cities. This project is adopting a multi-disciplinary approach that combines artificial intelligence techniques with psychology research to monitor the current state of the city of L’Aquila after the dreadful earthquake of April 2009. This work focuses on the description of a semantic content analysis module. This component, integrated into L’Aquila Social Urban Network (SUN), combines Natural Language Processing (NLP) and Artificial Intelligence (AI) to deeply analyze the content produced by citizens on social platforms in order to map social data with social indicators such as cohesion, sense of belonging and so on. The research carries on the insight that social data can supply a lot of information about latent people feelings, opinion and sentiments. Within the project, this trustworthy snapshot of the city is used by community promoters to proactively propose initiatives aiming at empowering the social capital of the city and recovering the urban structure which has been disrupted after the ’diaspora’ of citizens in the so called ”new towns”.

This paper investigates the role of Distributional Semantic Models (DSMs) into a Question Answering (QA) system. Our purpose is to exploit DSMs for answer re-ranking in QuestionCube, a framework for building QA systems. DSMs model words as points in a geometric space, also known as semantic space. Words are similar if they are close in that space. Our idea is that DSMs approaches can help to compute relatedness between users’ questions and candidate answers by exploiting paradigmatic relations between words, thus providing better answer reranking. Results of the evaluation, carried out on the CLEF2010 QA dataset, prove the effectiveness of the proposed approach.

Personalized electronic program guides help users overcome information overload in the TV and video domain by exploiting recommender systems that automatically compile lists of novel and diverse video assets, based on implicitly or explicitly defined user preferences. In this context, we assume that user preferences can be specified by program genres (documentary, sports, ...) and that an asset can be labeled by one or more program genres, thus allowing an initial and coarse preselection of potentially interesting assets. As these assets may come from various sources, program genre labels may not be consistent among these sources, or not even be given at all, while we assume that each asset has a possibly short textual description. In this paper, we tackle this problem by considering whether those textual descriptions can be effectively used to automatically retrieve the most related TV shows for a specific program genre. More specifically, we compare a statistical approach called logistic regression with an enhanced version of the commonly used vector space model, called random indexing, where the latter is extended by means of a negation operator based on quantum logic. We also apply a new feature generation technique based on explicit semantic analysis for enriching the textual description associated to a TV show with additional features extracted from Wikipedia.

The recent explosion of Big Data is offering new chances and challenges to all those platforms that provide personalized access to information sources, such as recommender systems and personalized search engines. In this context, social networks are gaining more and more interests since they represent a perfect source to trigger personalization tasks. Indeed, users naturally leave on these platforms a lot of data about their preferences, feelings, and friendships. Hence, those data are really valuable for addressing the cold start problem of recommender systems. On the other hand, since content shared on social networks is noisy and heterogeneous, information extracted must be hardly processed to build user profiles that can effectively mirror user interests and needs. In this paper we investigated the effectiveness of external knowledge derived from Wikipedia in representing both documents and user profiles in a recommendation scenario. Specifically, we compared a classical keyword-based representation with two techniques that are able to map unstructured text with Wikipedia pages. The advantage of using this representation is that documents and user profiles become richer, more human-readable, less noisy, and potentially connected to the Linked Open Data (LOD) cloud. The goal of our preliminary experimental evaluation was twofolds: 1) to define the representation that best reflects user preferences; 2) to define the representation that provides the best predictive accuracy. We implemented a news recommender for a preliminary evaluation of our model. We involved more than 50 Facebook and Twitter users and we demonstrated that the encyclopedic-based representation is an effective way for modeling both user profiles and documents.

This paper investigates the role of Distributional Semantic Models (DSMs) in Question Answering (QA), and specifically in a QA system called QuestionCube. QuestionCube is a framework for QA that combines several techniques to retrieve passages containing the exact answers for natural language questions. It exploits Information Retrieval models to seek candidate answers and Natural Language Processing algorithms for the analysis of questions and candidate answers both in English and Italian. The data source for the answer is an unstructured text document collection stored in search indices. In this paper we propose to exploit DSMs in the QuestionCube framework. In DSMs words are represented as mathematical points in a geometric space, also known as semantic space. Words are similar if they are close in that space. Our idea is that DSMs approaches can help to compute relatedness between users’ questions and candidate answers by exploiting paradigmatic relations between words. Results of an experimental evaluation carried out on CLEF2010 QA dataset, prove the effectiveness of the proposed approach.

Recommender Systems have already proved to be valuable for coping with the information overload problem in several application domains. They provide people with suggestions for items which are likely to be of interest for them; hence, a primary function of recommender systems is to help people make good choices and decisions. However, most previous research has focused on recommendation techniques and algorithms, and less attention has been devoted to the decision making processes adopted by the users and possibly supported by the system. There is still a gap between the importance that the community gives to the assessment of recommendation algorithms and the current range of ongoing research activities concerning human decision making. Different decision-psychological phenomena can influence the decision making of users of recommender systems, and research along these lines is becoming increasingly important and popular. This special issue highlights how the coupling of recommendation algorithms with the understanding of human choice and decision making theory has the potential to benefit research and practice on recommender systems and to enable users to achieve a good balance between decision accuracy and decision effort.

Throughout the last decade, the area of Digital Libraries (DL) get more and more interest from both the research and development communities. Likewise, since the release of new platforms enriches them with new features and makes DL more powerful and effective, the number of web sites integrating these kind of tools is rapidly growing. In this paper we propose an approach for the exploitation of digital libraries for personalization goal in cultural heritage scenario. Specifically, we tried to integrate FIRSt (Folksonomy-based Item Recommender syStem), a content-based recommender system developed at the University of Bari, and Fedora, a flexible digital library architecture, in a framework for the adaptive fruition of cultural heritage implemented within the activities of the CHAT research project. In this scenario, the role of the digital library was to store information (such as textual and multimedial ones) about paintings gathered from the Vatican Picture Gallery and to provide them in a multimodal and personalized way through a PDA device given to a user before her visit in a museum. This paper describes the system architecture of our recommender system and its integration in the framework implemented for the CHAT project, showing how this recommendation model has been applied to recommend the artworks located at the Vatican Picture Gallery (Pinacoteca Vaticana), providing users with a personalized museum tour tailored on their tastes. The experimental evaluation we performed also confirmed that these recommendation services are really able to catch the real user preferences thus improving their experience in cultural heritage fruition.

As an interactive intelligent system, recommender systems are developed to suggest items that match users’ preferences. Since the emergence of recommender systems, a large majority of research has focused on objective accuracy criteria and less attention has been paid to how users interact with the system and the efficacy of interface designs from users’ perspectives. The field has reached a point where it is ready to look beyond algorithms, into users’ interactions, decision making processes and overall experience. Accordingly, the goals of the workshop are to explore the human aspects of recommender systems, with a particular focus on the impact of interfaces and interaction design on decision-making and user experiences with recommender systems, and to explore methodologies to evaluate these human aspects of the recommendation process that go beyond traditional automated approaches. The aim is to bring together researchers and practitioners around the topics of designing and evaluating novel intelligent interfaces for recommender systems in order to: (1) share research and techniques, including new design technologies and evaluation methodologies (2) identify next key challenges in the area, and (3) identify emerging topics. The workshop covers three interrelated themes: a) user interfaces (e.g. visual interfaces, explanations), b) interaction, user modeling and decision-making (e.g. decision theories, argumentation, detection and avoidance of biases), and c) evaluation (e.g. case studies and empirical evaluations). This workshop aims at creating an interdisciplinary community with a focus on the interface design issues for recommender systems and promoting collaboration opportunities between researchers and practitioners. The workshop consists of a mix of eight presentations of papers in which results of ongoing research as reported in these proceedings are presented and one invited talk by Julita Vassileva presenting “Visualization and User Control of Recommender Systems”. The workshop is closed by a final discussion session.

With the increasing amount of information in electronic form the fields of Machine Learning and Data Mining continue to grow by providing new advances in theory, applications and systems. The aim of this paper is to consider some recent theoretical aspects and approaches to ML and DM with an emphasis on the Italian research.

The exponential growth of the Web is the most influential factor that contributes to the increasing importance of cross-lingual text retrieval and filtering systems. Indeed, relevant information exists in different languages, thus users need to find documents in languages different from the one the query is formulated in. In this context, an emerging requirement is to sift through the increasing flood of multilingual text: this poses a renewed challenge for designing effective multilingual Information Filtering systems. Content-based filtering systems adapt their behavior to individual users by learning their preferences from documents that were already deemed relevant. The learning process aims to construct a profile of the user that can be later exploited in selecting/recommending relevant items. User profiles are generally represented using keywords in a specific language. For example, if a user likes movies whose plots are written in Italian, a content-based filtering algorithm will learn a profile for that user which contains Italian words, thus failing in recommending movies whose plots are written in English, although they might be definitely interesting. Moreover, keywords suffer of typical Information Retrieval-related problems such as polysemy and synonymy. In this paper, we propose a language-independent content-based recommender system, called MARS (MultilAnguage Recommender System), that builds cross-language user profiles, by shifting the traditional text representation based on keywords, to a more complex language-independent representation based on word meanings. The proposed strategy relies on a knowledge-based word sense disambiguation technique that exploits MultiWordNet as sense inventory. As a consequence, content-based user profiles become language-independent and can be exploited for recommending items represented in a language different from the one used in the content-based user profile. Experiments conducted in a movie recommendation scenario show the effectiveness of the approach.

This paper presents MyMusic, a system that exploits social media sources for generating personalized music playlists. This work is based on the idea that information extracted from social networks, such as Facebook and Last.fm, might be effectively exploited for personalization tasks. Indeed, information related to music preferences of users can be easily gathered from social platforms and used to define a model of user interests. The use of social media is a very cheap and effective way to overcome the classical cold start problem of recommender systems. In this work we enriched social media-based playlists with new artists related to those the user already likes. Specically, we compare two different enrichment techniques: the first leverages the knowledge stored on DBpedia, the structured version of Wikipedia, while the second is based on the content-based similarity between descriptions of artists. The final playlist is ranked and finally presented to the user that can listen to the songs and express her feedbacks. A prototype version of MyMusic was made available online in order to carry out a preliminary user study to evaluate the best enrichment strategy. The preliminary results encouraged keeping on this research.

In the last years, hundreds of social networks sites have been launched with both professional (e.g., LinkedIn) and non-professional (e.g., MySpace, Facebook) orientations. This resulted in a renewed information overload problem, but it also provided a new and unforeseen way of gathering useful, accurate and constantly updated information about user interests and tastes. Content-based recommender systems can leverage the wealth of data emerging by social networks for building user profiles in which representations of the user interests are maintained. The idea proposed in this paper is to extract content-based user profiles from the data available in the LinkedIn social network, to have an image of the users' interests that can be used to recommend interesting academic research papers. A preliminary experiment provided interesting results which deserve further attention.

The exponential growth of the Web is the most influential factor that contributes to the increasing importance of cross-lingual text retrieval and filtering systems. Indeed, relevant information exists in different languages, thus users need to find documents in languages different from the one the query is formulated in. In this context, an emerging requirement is to sift through the increasing flood of multilingual text: this poses a renewed challenge for designing effective multilingual Information Filtering systems. In this paper, we propose a language-independent content-based recommender system, called MARS (MultilAnguage Recommender System), that builds cross-language user profiles, by shifting the traditional text representation based on keywords, to a more complex language-independent representation based on word meanings. As a consequence, the recommender system is able to suggest items represented in a language different from the one used in the content-based user profile. Experiments conducted in a movie recommendation scenario show the effectiveness of the approach.

Recommender Systems suggest items that are likely to be the most interesting for users, based on the feedback, i.e. ratings, they provided on items already experienced in the past. Time-aware Recommender Systems (TARS) focus on temporal context of ratings in order to track the evolution of user preferences and to adapt suggestions accordingly. In fact, some people's interests tend to persist for a long time, while others change more quickly, because they might be related to volatile information needs. In this paper, we focus on the problem of building an effective profile for short-term preferences. A simple approach is to learn the short-term model from the most recent ratings, discarding older data. It is based on the assumption that the more recent the data is, the more it contributes to find items the user will shortly be interested in. We propose an improvement of this classical model, which tracks the evolution of user interests by exploiting the content of the items, besides time information on ratings. When a new item-rating pair comes, the replacement of an older one is performed by taking into account both a decay function for user interests and content similarity between items, computed by distributional semantics models. Experimental results confirm the effectiveness of the proposed approach.

Recommender Systems try to assist users to access complex information spaces regarding their long term needs and preferences. Various recommendation techniques have been investigated and each one has its own strengths and weaknesses. Especially, content-based techniques suffer of overspecialization problem. We propose to inject diversity in the recommendation task by exploiting the content-based user profile to spot potential surprising suggestions. In addition, the actual selection of serendipitous items is motivated by an applicative scenario. Thus, the reference scenario concerns personalized tours in a museum and serendipitous items are introduced by slight diversions on the context-aware tours.

This paper describes OTTHO (On the Tip of my THOught), a system designed for solving a language game, called Guillotine. The rule of the game is simple: the player observes five words, generally unrelated to each other, and in one minute she has to provide a sixth word, semantically connected to the others. The system performs retrieval from several knowledge sources, such as a dictionary, a set of proverbs, and Wikipedia to realize a knowledge infusion process. The main motivation for designing an artificial player for Guillotine is the challenge of providing the machine with the cultural and linguistic background knowledge which makes it similar to a human being, with the ability of interpreting natural language documents and reasoning on their content. Our feeling is that the approach presented in this work has a great potential for other more practical applications besides solving a language game.

This paper describes OTTHO (On the Tip of my THOught), an artificial player able to solve a very popular language game, called “The Guillotine”, broadcast by the Italian National TV company. The game demands knowledge covering a broad range of topics, such as politics, literature, history, proverbs, and popular culture. The rule of the game is simple: the player observes five words, generally unrelated to each other, and in one minute she has to provide a sixth word, semantically connected to the others. In order to find the solution, a human being has to perform a complex memory retrieval task within the facts retained in her own knowledge, concerning the meanings of thousands of words and their contextual relations. In order to make this task executable by machines, machine reading techniques are exploited for knowledge extraction from the web, while Artificial Intelligence techniques are used to infer new knowledge, in the form of keywords, from the extracted information.

Recommendation of financial investment strategies is a complex and knowledge-intensive task. Typically, financial advisors have to discuss at length with their wealthy clients and have to sift through several investment proposals before finding one able to completely meet investors' needs and constraints. As a consequence, a recent trend in wealth management is to improve the advisory process by exploiting recommendation technologies. This paper proposes a framework for recommendation of asset allocation strategies which combines case-based reasoning with a novel diversification strategy to support financial advisors in the task of proposing diverse and personalized investment portfolios. The performance of the framework has been evaluated by means of an experimental session conducted against 1172 real users, and results show that the yield obtained by recommended portfolios overcomes that of portfolios proposed by human advisors in most experimental settings while meeting the preferred risk profile. Furthermore, our diversification strategy shows promising results in terms of both diversity and average yield.

This paper describes the techniques used to build a virtual player for the popular TV game "Who Wants to Be a Millionaire?". The player must answer a series of multiple-choice questions posed in natural language by selecting the correct answer among four different choices. The architecture of the virtual player consists of 1) a Question Answering (QA) module, which leverages Wikipedia and DBpedia datasources to retrieve the most relevant passages of text useful to identify the correct answer to a question, 2) an Answer Scoring (AS) module, which assigns a score to each candidate answer according to different criteria based on the passages of text retrieved by the Question Answering module, and 3) a Decision Making (DM) module, which chooses the strategy for playing the game according to specific rules as wellas to the scores assigned to the candidate answers.We have evaluated both the accuracy of the virtual player to correctly answer to questions of the game, and its ability to play real games in order to earn money. The experiments have been carried out on questions comingfrom the official Italian and English boardgames. The average accuracy of the virtual player for Italian is 79.64%, which is significantly better than the performance of human players, which is equal to 51.33%. The average accuracy of the virtual player for English is 76.41%. The comparison with human players is not carried out for English since, playing successfully the game heavily depends on the players' knowledge about popular culture, and in this experiment we have only involved a sample of Italian players. As regards the ability to play real games, which involves the definition of a proper strategy for the usage of lifelines in order to decide whether to answer to a question even in a condition of uncertainty or to retire from the game by taking the earned money, the virtual player earns € 114,531 on average for Italian, and E 88,878 for English, which exceeds the average amount earned by the human players to a greater extent (€ 5,926 for Italian).

These are the proceedings of the First Workshop on Semantic Technologies meet Recommender Systems & Big Data (SeRSy 2012), held in conjunction with the 11th International Semantic Web Conference (ISWC 2012). People generally need more and more advanced tools that go beyond those implementing the canonical search paradigm for seeking relevant information. A new search paradigm is emerging, where the user perspective is completely reversed: from finding to being found. Recommender Systems may help to support this new perspective, because they have the effect of pushing relevant objects, selected from a large space of possible options, to potentially interested users. To achieve this result, recommendation techniques generally rely on data referring to three kinds of objects: users, items and their relations. The widespread success of Semantic Web techniques, creating a Web of interoperable and machine readable data, can be also beneficial for recommender systems. Indeed, more and more semantic data are published following the Linked Data principles, that enable to set up links between objects in different data sources, by connecting information in a single global data space – the Web of Data. Today, the Web of Data includes different types of knowledge represented in a homogeneous form – sedimentary one (encyclopedic, cultural, linguistic, common-sense, …) and real-time one (news, data streams, …). This data might be useful to interlink diverse information about users, items, and their relations and implement reasoning mechanisms that can support and improve the recommendation process. The challenge is to investigate whether and how this large amount of wide-coverage and linked semantic knowledge can significantly improve the search process in those tasks that cannot be solved merely through a straightforward matching of queries and documents. Such tasks involve finding information from large document collections, categorizing and understanding that information, and producing some product, such as an actionable decision. Examples of such tasks include understanding a health problem in order to make a medical decision, or simply deciding which laptop to buy. Recommender systems support users exactly in those complex tasks. The primary goal of the workshop is to showcase cutting edge research in the intersection of semantic technologies and recommender systems, by taking the best of the two worlds. This combination may provide the Semantic Web community with important realworld scenarios where its potential can be effectively exploited into systems performing complex tasks. We wish to thank all authors who submitted papers and all workshop participants for fruitful discussions. We would like to thank the program committee members and external referees for their timely expertise in carefully reviewing the submissions. We would also like to thank our invited speaker Ora Lassila for his interesting and stimulating talk. October 2012 The Workshop Chairs Marco de Gemmis Tommaso Di Noia Pasquale Lops Thomas Lukasiewicz Giovanni Semeraro

Interacting with a recommender system means to take different decisions such as selecting a song/movie from a recommendation list, selecting specific feature values (e.g., camera’s size, zoom) as criteria, selecting feedback features to be critiqued in a critiquing based recommendation session, or selecting a repair proposal for inconsistent user preferences when interacting with a knowledge-based recommender. In all these scenarios, users have to solve a decision task. The complexity of decision tasks, limited cognitive resources of users, and the tendency to keep the overall decision effort as low as possible lead to the phenomenon of bounded rationality, i.e., users exploit decision heuristics rather than trying to take an optimal decision. Furthermore, preferences of users will likely change throughout a recommendation session, i.e., preferences are constructed in a specific decision environment and users do not know their preferences beforehand. Decision making under bounded rationality is a door opener for different types of non-conscious influences on the decision behavior of a user. Theories from decision psychology and cognitive psychology are trying to explain these influences, for example, decoy effects and defaults can trigger significant shifts in item selection probabilities; in group decision scenarios, the visibility of the preferences of other group members can have a significant impact on the final group decision. The major goal of this workshop was to establish a platform for industry and academia to present and discuss new ideas and research results that are related to the topic of human decision making in recommender systems. The workshop consisted of technical sessions in which results of ongoing research as reported in these proceedings were presented, a keynote talk given by Joseph A. Konstan on “Decision-Making and Recommender Systems: Failures, Successes, and Research Directions” and a wrap up session chaired by Alexander Felfernig.

The vector space model (VSM) emerged for almost three decades as one of the most effective approaches in the area of Information Retrieval (IR), thanks to its good compromise between expressivity, effectiveness and simplicity. Although Information Retrieval and Information Filtering (IF) undoubtedly represent two related research areas, the use of VSM in Information Filtering is much less analyzed, especially for content-based recommender systems. The goal of this work is twofold: first, we investigate the impact of VSM in the area of content-based recommender systems; second, since VSM suffer from well-known problems, such as its high dimensionality and the inability to manage information coming from negative user preferences, we propose techniques able to effectively tackle these drawbacks. Specifically we exploited Random Indexing for dimensionality reduction and the negation operator implemented in the Semantic Vectors open source package to model negative user preferences. Results of an experimental evaluation performed on these enhanced vector space models (eVSM) and the potential applications of these approaches confirm the effectiveness of the model and lead us to further investigate these techniques.

Interacting with a recommender system means to take different decisions such as selecting an item from a recommendation list, selecting a specific item feature value (e.g., camera’s size, zoom) as a search criteria, selecting feedback features to be critiqued in a critiquing based recommendation session, or selecting a repair proposal for inconsistent user preferences when interacting with a knowledge-based recommender. In all these situations, users face a decision task. This workshop (Decisions@RecSys) focuses on approaches for supporting effective and efficient human decision making in different types of recommendation scenarios.

As an interactive intelligent system, recommender systems are developed to give predictions that match users preferences. Since the emergence of recommender systems, a large majority of research focuses on objective accuracy criteria and less attention has been paid to how users interact with the system and the efficacy of interface designs from the end-user perspective. The field has reached a point where it is ready to look beyond algorithms, into users interactions, decision-making processes and overall experience. Accordingly, the goals of this workshop (IntRS@RecSys) are to explore the human aspects of recommender systems, with a particular focus on the impact of interfaces and interaction design on decision-making and user experiences with recommender systems, and to explore methodologies to evaluate these human aspects of the recommendation process that go beyond traditional automated approaches.

Artificial Intelligence technologies are growingly used within several software systems ranging from Web services to mobile applications. It is by no doubt true that the more AI algorithms and methods are used the more they tend to depart from a pure "AI" spirit and end to refer to the sphere of standard software. In a sense, AI seems strongly connected with ideas, methods and tools that are not (yet) used by the general public. On the contrary, a more realistic view of it would be a rich and pervading set of successful paradigms and approaches. Industry is currently perceiving semantic technologies as a key contribution of AI to innovation. In this paper a survey of current industrial experiences is used to discuss different semantic technologies at work in heterogeneous areas, ranging from Web services to semantic search and recommender systems.The resulting picture confirms the vitality of the area and allows to sketch a general taxonomy of approaches, that is the main contribution of this paper.

Today Recommender Systems (RSs) are commonly used with various purposes, especially dealing with e-commerce and information filtering tools. Content-based RSs rely on the concept of similarity between items. It is a common belief that the user is interested in what is similar to what she has already bought/searched/visited. We believe that there are some contexts in which this assumption is wrong: it is the case of acquiring unsearched but still useful items or pieces of information. This is called serendipity. Our purpose is to stimulate users and facilitate these serendipitous encounters to happen. The paper presents a hybrid recommender system that joins a content-based approach and serendipitous heuristics in order to provide also surprising suggestions. The reference scenario concerns with personalized tours in a museum and serendipitous items are introduced by slight diversions on the context-aware tours. Copyright owned by the authors.

“The Guillotine” is a language game whose goal is to predict the unique word that is linked in some way to five words given as clues, generally unrelated to each other. The ability of the human player to find the solution depends on the richness of her cultural background. We designed an artificial player for that game, based on a large knowledge repository built by exploiting several sources available on the web, such as Wikipedia, that provide the system with the cultural and linguistic background needed to understand clues. The “brain” of the system is a spreading activation algorithm that starts processing clues, finds associations between them and words within the knowledge repository, and computes a list of candidate solutions. In this paper we focus on the problem of finding the most promising candidate solution to be provided as the final answer. We improved the spreading algorithm by means of two strategies for finding associations also between candidate solutions and clues. Those strategies allow bidirectional reasoning and select the candidate solution which is the most connected with the clues. Experiments show that the performance of the system is comparable to that of average human players.

A primary function of recommender systems is to help their users to make better choices and decisions. The overall goal of the workshop is to analyse and discuss novel techniques and approaches for supporting effective and efficient human decision making in different types of recommendation scenarios. The submitted papers discuss a wide range of topics, from core algorithmic issues to the management of the human computer interaction.