Abstract
User preferences are a crucial input needed by recommender systems to determine relevant items. In single-shot recommendation scenarios such as content-based filtering and collaborative filtering, user preferences are represented, for example, as keywords, categories, and item ratings. In conversational recommendation approaches such as constraint-based and critiquing-based recommendation, user preferences are often represented on the semantic level in terms of item attribute values and critiques. In this article, we provide an overview of preference representations used in different types of recommender systems. In this context, we take into account the fact that preferences aren’t stable but are rather constructed within the scope of a recommendation process. In which way preferences are determined and adapted is influenced by various factors such as personality traits, emotional states, and cognitive biases. We summarize preference construction related research and also discuss aspects of counteracting cognitive biases.
Highlights
Recommender systems help to identify items of relevance for a user (Burke et al, 2011; Felfernig et al, 2018)
Group recommenders (Garcia et al, 2012), but provides an integrated overview of preference construction in recommender systems. – In addition to a summarization of the state-of-the-art in preference construction, we focus on discussing specific psychological aspects and related contextual factors that have to be taken into account when implementing a recommender system to assure a high quality of decision support. – In order to show the relevance of the discussed psychological aspects for preference construction, we exemplify the existence of decision biases in software engineering scenarios on the basis of an empirical study
We point out ways to counteract these biases. – With the goal to stimulate further research in preference construction related areas, we provide a discussion of open research issues
Summary
Extended author information available on the last page of the article. N-point Likert scale (e.g., a 1–5 star rating), likes/dislikes, and attribute values in constraintbased recommendation. Felfernig et al (2018) provide an overview of different decision biases in the context of group recommendation scenarios, for example, anchoring effects (Jacowitz & Kahneman, 1995) can occur in situations where the preferences of some group members are disclosed too early which can influence the preference construction process of other group members. – In order to show the relevance of the discussed psychological aspects for preference construction, we exemplify the existence of decision biases in software engineering scenarios on the basis of an empirical study. In this context, we point out ways to counteract these biases.
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