Humanized Recommender Systems: State-of-the-art and Research Issues

Abstract

Psychological factors such as personality, emotions, social connections , and decision biases can significantly affect the outcome of a decision process. These factors are also prevalent in the existing literature related to the inclusion of psychological aspects in recommender system development. Personality and emotions of users have strong connections with their interests and decision-making behavior. Hence, integrating these factors into recommender systems can help to better predict users’ item preferences and increase the satisfaction with recommended items. 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. Decision biases often occur in a recommendation process, since users usually apply heuristics when making a decision. These biases can result in low-quality decisions. In this article, we provide a rigorous review of existing research on the influence of the mentioned psychological factors on recommender systems. 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. We include working examples to provide a deeper understanding of how to take into account these factors in recommendation processes. The provided examples go beyond single-user recommendation scenarios by also considering specific aspects of group recommendation settings.

Recommender systems for sustainability: overview and research issues

“…Successful nudges are often based on decision biases, i.e., decision practices (heuristics) used by humans to often lead to suboptimal decision outcomes. An overview of such decision biases and their role in recommender systems is discussed in Mandl et al ( 2011 ), Chen et al ( 2013 ), Lex et al ( 2021 ), and Tran et al ( 2021 ).…”

Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems

“…Those segments of the population that suffer the negative consequences of bias are the so-called protected groups on which mitigation strategies should focus. All of these facts have driven the research currently being carried out in this field [5,[15][16][17].…”

Deep Learning-Driven Multi-Criteria Decision-Making for Effective Recommender Systems

“…This approach results in a larger amount of data being collected, enabling the system to provide more appropriate suggestions. The additional criteria also provide more insights into user preferences, which can be useful for improving recommendation accuracy and understanding user behavior [10]. Users often evaluate items based on various criteria, and using only the overall rating for a user-item pair may not be effective.…”