Recent Developments in Recommender Systems: A Survey. (arXiv:2306.12680v1 [cs.IR])
By: <a href="http://arxiv.org/find/cs/1/au:+Li_Y/0/1/0/all/0/1">Yang Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_K/0/1/0/all/0/1">Kangbo Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Satapathy_R/0/1/0/all/0/1">Ranjan Satapathy</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_S/0/1/0/all/0/1">Suhang Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Cambria_E/0/1/0/all/0/1">Erik Cambria</a> Posted: June 23, 2023
In this technical survey, we comprehensively summarize the latest
advancements in the field of recommender systems. The objective of this study
is to provide an overview of the current state-of-the-art in the field and
highlight the latest trends in the development of recommender systems. The
study starts with a comprehensive summary of the main taxonomy of recommender
systems, including personalized and group recommender systems, and then delves
into the category of knowledge-based recommender systems. In addition, the
survey analyzes the robustness, data bias, and fairness issues in recommender
systems, summarizing the evaluation metrics used to assess the performance of
these systems. Finally, the study provides insights into the latest trends in
the development of recommender systems and highlights the new directions for
future research in the field.
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