Human Decision Making in Recommender Systems

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Abstract

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.

Autore Pugliese

Tutti gli autori

  • SEMERARO G.;de GEMMIS M.;LOPS P.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2013

ISSN

1613-0073

ISBN

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Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

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Numero di citazioni Scopus

7

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

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Codici ASJC

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