A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929410122186752 |
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| author | Deldjoo, Yashar He, Zhankui McAuley, Julian Korikov, Anton Sanner, Scott Ramisa, Arnau Vidal, René Sathiamoorthy, Maheswaran Kasirzadeh, Atoosa Milano, Silvia |
| author_facet | Deldjoo, Yashar He, Zhankui McAuley, Julian Korikov, Anton Sanner, Scott Ramisa, Arnau Vidal, René Sathiamoorthy, Maheswaran Kasirzadeh, Atoosa Milano, Silvia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_00579 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys) Deldjoo, Yashar He, Zhankui McAuley, Julian Korikov, Anton Sanner, Scott Ramisa, Arnau Vidal, René Sathiamoorthy, Maheswaran Kasirzadeh, Atoosa Milano, Silvia Information Retrieval Artificial Intelligence 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. |
| title | A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys) |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2404.00579 |