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Tailoring the recommendation of tourist information to heterogeneous user groups. Group recommendation: Semantics and efficiency. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. Do recommender systems manipulate consumer preferences? A study of anchoring effects. De-biasing user preference ratings in recommender systems. An emotion-aware personalized music recommendation system using a convolutional neural networks approach. In Proceedings of the 11th ACM Conference on Recommender Systems (RecSys’17). Controlling popularity bias in learning-to-rank recommendation. The provided examples go beyond single-user recommendation scenarios by also considering specific aspects of group recommendation settings.
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We include working examples to provide a deeper understanding of how to take into account these factors in recommendation processes. These factors are not only considered in single-user recommendation scenarios but, importantly, also in group recommendation ones, where groups of users are involved in a decision-making process. In this article, we provide a rigorous review of existing research on the influence of the mentioned psychological factors on recommender systems. These biases can result in low-quality decisions. Decision biases often occur in a recommendation process, since users usually apply heuristics when making a decision. In scenarios where decisions are made by groups (e.g., selecting a tourism destination to visit with friends), group composition and social connections among group members can affect the outcome of a group decision. Hence, integrating these factors into recommender systems can help to better predict users’ item preferences and increase the satisfaction with recommended items. Personality and emotions of users have strong connections with their interests and decision-making behavior. These factors are also prevalent in the existing literature related to the inclusion of psychological aspects in recommender system development. Psychological factors such as personality, emotions, social connections, and decision biases can significantly affect the outcome of a decision process.
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