A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
arxiv(2024)
摘要
Traditional recommender systems (RS) have used user-item rating histories as
their primary data source, with collaborative filtering being one of the
principal methods. However, generative models have recently developed abilities
to model and sample from complex data distributions, including not only
user-item interaction histories but also text, images, and videos - unlocking
this rich data for novel recommendation tasks. Through this comprehensive and
multi-disciplinary survey, we aim to connect the key advancements in RS using
Generative Models (Gen-RecSys), encompassing: a foundational overview of
interaction-driven generative models; the application of large language models
(LLM) for generative recommendation, retrieval, and conversational
recommendation; and the integration of multimodal models for processing and
generating image and video content in RS. Our holistic perspective allows us to
highlight necessary paradigms for evaluating the impact and harm of Gen-RecSys
and identify open challenges. A more up-to-date version of the papers is
maintained at: https://github.com/yasdel/LLM-RecSys.
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