Trustworthy Recommendation and Search Introduction to the Special Issue - Part 1

Recommendation and search systems have already become indispensable means for helping web users identify the most relevant information/services in the era of information overload. The applications of such systems are multi-faceted, including targeted advertising, intelligent medical assistant, and e-commerce, and are bringing immense convenience to people’s daily lives. However, despite rapid advances in recommendation and search, the increasing public awareness of the trustworthiness of relevant recommendation and search applications has introduced higher expectations on relevant research. Firstly, the unprecedentedly growing heterogeneity of use cases has been challenging the adaptivity of contemporary algorithms to various settings, e.g., dynamic user interests [Chen et al. 2019], highly sparse interaction records [Chen et al. 2020b], and limited computing resources [Long et al. 2022; Imran et al. 2022]. Secondly, in a broader sense, a trustworthy recommendation/search approach should also be robust, interpretable, secure, privacy-preserving, and fair across different use cases. Specifically, robustness evaluates a model’s performance consistency under various operating conditions like noisy data [Zhang et al. 2020]; interpretability and fairness respectively evaluate if a model can make its decision processes transparent [Chen et al. 2020c;, 2021; Lyu et al. 2021; Cui et al. 2022; Ren et al. 2021] and the decision outcomes unbiased [Chen et al. 2020a; Li et al. 2021; Yin et al. 2012]; while security and privacy respectively emphasize a model’s ability to handle cyber-attacks [Zhang et al. 2021b;, 2022] and to prevent personal information leakage [Zhang and Yin 2022; Zhang et al. 2021c;, 2021a; Yuan et al. 2023; Wang et al. 2022b]. Consequently, trustworthiness is becoming a key performance indicator for state-of-the-art recommendation and search approaches. In light of these emerging challenges, this special section focuses on novel research in this field with the notion of trustworthiness. The articles presented in this special issue will further promote responsible AI applications, thus better universalizing the advanced techniques to a wider range of the common public.