A survey on multi-objective recommender systems

Frontiers in Big Data

Big Data

Dietmar Jannach¹  Himan Abdollahpouri² 

[1] Department of Artificial Intelligence and Cybersecurity, University of Klagenfurt, Klagenfurt, Austria;Spotify, Inc., New York, NY, United States;

关键词: recommender systems;    evaluation;    multistakeholder recommendation;    beyond-accuracy optimization;    short-term and long-term objectives;   

DOI  :  10.3389/fdata.2023.1157899

 received in 2023-02-03, accepted in 2023-03-07,  发布年份 2023

来源: Frontiers

【 摘 要 】

Recommender systems can be characterized as software solutions that provide users with convenient access to relevant content. Traditionally, recommender systems research predominantly focuses on developing machine learning algorithms that aim to predict which content is relevant for individual users. In real-world applications, however, optimizing the accuracy of such relevance predictions as a single objective in many cases is not sufficient. Instead, multiple and often competing objectives, e.g., long-term vs. short-term goals, have to be considered, leading to a need for more research in multi-objective recommender systems. We can differentiate between several types of such competing goals, including (i) competing recommendation quality objectives at the individual and aggregate level, (ii) competing objectives of different involved stakeholders, (iii) long-term vs. short-term objectives, (iv) objectives at the user interface level, and (v) engineering related objectives. In this paper, we review these types of multi-objective recommendation settings and outline open challenges in this area.1

【 授权许可】

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Copyright © 2023 Jannach and Abdollahpouri.