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A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)

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arxiv 2404.00579 v2 pith:GLUHCQ2J submitted 2024-03-31 cs.IR cs.AI

A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)

Yashar Deldjoo , Zhankui He , Julian McAuley , Anton Korikov , Scott Sanner , Arnau Ramisa , Ren\'e Vidal , Maheswaran Sathiamoorthy
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Atoosa Kasirzadeh Silvia Milano
This is my paper
classification cs.IR cs.AI
keywords modelsgenerativedatagen-recsysimageslanguagerecommendationrecommender
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Traditional recommender systems (RS) typically use user-item rating histories as their main data source. However, deep generative models now have the capability to model and sample from complex data distributions, including user-item interactions, text, images, and videos, enabling novel recommendation tasks. This comprehensive, multidisciplinary survey connects key advancements in RS using Generative Models (Gen-RecSys), covering: interaction-driven generative models; the use of large language models (LLM) and textual data for natural language recommendation; and the integration of multimodal models for generating and processing images/videos in RS. Our work highlights necessary paradigms for evaluating the impact and harm of Gen-RecSys and identifies open challenges. This survey accompanies a tutorial presented at ACM KDD'24, with supporting materials provided at: https://encr.pw/vDhLq.

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Cited by 3 Pith papers

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  1. Generative Long-term User Interest Modeling for Click-Through Rate Prediction

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    GenLI generates diverse target-independent interest distributions via an IGM, retrieves behaviors with O(1) lookup in BRM, and fuses via IFM gating to balance accuracy and efficiency in CTR prediction.

  2. Prompt-Adapter Context Routing for Parameter-Efficient Multi-Shot Long Video Extrapolation

    cs.CV 2026-07 conditional novelty 5.0

    A frozen video diffusion backbone augmented with low-rank temporal adapters and a recursive prompt bank outperforms prior long-video generation methods on six benchmarks while tuning only 3.8% of parameters.

  3. Retrieval-Augmented Generation with Graphs (GraphRAG)

    cs.IR 2024-12 unverdicted novelty 5.0

    A survey proposing a holistic GraphRAG framework with components including query processor, retriever, organizer, generator, and data source, plus domain-tailored reviews, challenges, and future directions.