Related papers: A Survey on Sequential Recommendation

A Survey on Sequential Recommendation

URL: http://arxiv.org/abs/2412.12770v1
Date: Tue, 17 Dec 2024 10:33:13 GMT
Title: A Survey on Sequential Recommendation
Authors: Liwei Pan, Weike Pan, Meiyan Wei, Hongzhi Yin, Zhong Ming,
Abstract summary: We study the SR problem from a new perspective (i.e., the construction of an item's properties) We summarize the most recent techniques used in sequential recommendation such as pure ID-based SR, SR with side information, multi-modal SR, generative SR, LLM-powered SR, ultra-long SR and data-augmented SR. We introduce some frontier research topics in sequential recommendation, e.g., open-domain SR, data-centric SR, could-edge collaborative SR, continuous SR, SR for good, and explainable SR.
Score: 35.01216224159067
License:
Abstract: Different from most conventional recommendation problems, sequential recommendation focuses on learning users' preferences by exploiting the internal order and dependency among the interacted items, which has received significant attention from both researchers and practitioners. In recent years, we have witnessed great progress and achievements in this field, necessitating a new survey. In this survey, we study the SR problem from a new perspective (i.e., the construction of an item's properties), and summarize the most recent techniques used in sequential recommendation such as pure ID-based SR, SR with side information, multi-modal SR, generative SR, LLM-powered SR, ultra-long SR and data-augmented SR. Moreover, we introduce some frontier research topics in sequential recommendation, e.g., open-domain SR, data-centric SR, could-edge collaborative SR, continuous SR, SR for good, and explainable SR. We believe that our survey could be served as a valuable roadmap for readers in this field.

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