Authors: Weizhi Zhang, Yuanchen Bei, Liangwei Yang, Henry Peng Zou, Peilin Zhou, Aiwei Liu, Yinghui Li, Hao Chen, Jianling Wang, Yu Wang, Feiran Huang, Sheng Zhou, Jiajun Bu, Allen Lin, James Caverlee, Fakhri Karray, Irwin King, Philip S. Yu
Abstract: Cold-start problem is one of the long-standing challenges in recommender
systems, focusing on accurately modeling new or interaction-limited users or
items to provide better recommendations. Due to the diversification of internet
platforms and the exponential growth of users and items, the importance of
cold-start recommendation (CSR) is becoming increasingly evident. At the same
time, large language models (LLMs) have achieved tremendous success and possess
strong capabilities in modeling user and item information, providing new
potential for cold-start recommendations. However, the research community on
CSR still lacks a comprehensive review and reflection in this field. Based on
this, in this paper, we stand in the context of the era of large language
models and provide a comprehensive review and discussion on the roadmap,
related literature, and future directions of CSR. Specifically, we have
conducted an exploration of the development path of how existing CSR utilizes
information, from content features, graph relations, and domain information, to
the world knowledge possessed by large language models, aiming to provide new
insights for both the research and industrial communities on CSR. Related
resources of cold-start recommendations are collected and continuously updated
for the community in
https://github.com/YuanchenBei/Awesome-Cold-Start-Recommendation.
Source: http://arxiv.org/abs/2501.01945v1
