The 2nd Workshop on Recommendation with Generative Models
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911790857715712 |
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| author | Wang, Wenjie Zhang, Yang Lin, Xinyu Feng, Fuli Liu, Weiwen Liu, Yong Zhao, Xiangyu Zhao, Wayne Xin Song, Yang He, Xiangnan |
| author_facet | Wang, Wenjie Zhang, Yang Lin, Xinyu Feng, Fuli Liu, Weiwen Liu, Yong Zhao, Xiangyu Zhao, Wayne Xin Song, Yang He, Xiangnan |
| contents | The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users' personalized recommendations. This workshop serves as a platform for researchers to explore and exchange innovative concepts related to the integration of generative models into recommender systems. It primarily focuses on five key perspectives: (i) improving recommender algorithms, (ii) generating personalized content, (iii) evolving the user-system interaction paradigm, (iv) enhancing trustworthiness checks, and (v) refining evaluation methodologies for generative recommendations. With generative models advancing rapidly, an increasing body of research is emerging in these domains, underscoring the timeliness and critical importance of this workshop. The related research will introduce innovative technologies to recommender systems and contribute to fresh challenges in both academia and industry. In the long term, this research direction has the potential to revolutionize the traditional recommender paradigms and foster the development of next-generation recommender systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_04399 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The 2nd Workshop on Recommendation with Generative Models Wang, Wenjie Zhang, Yang Lin, Xinyu Feng, Fuli Liu, Weiwen Liu, Yong Zhao, Xiangyu Zhao, Wayne Xin Song, Yang He, Xiangnan Information Retrieval The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users' personalized recommendations. This workshop serves as a platform for researchers to explore and exchange innovative concepts related to the integration of generative models into recommender systems. It primarily focuses on five key perspectives: (i) improving recommender algorithms, (ii) generating personalized content, (iii) evolving the user-system interaction paradigm, (iv) enhancing trustworthiness checks, and (v) refining evaluation methodologies for generative recommendations. With generative models advancing rapidly, an increasing body of research is emerging in these domains, underscoring the timeliness and critical importance of this workshop. The related research will introduce innovative technologies to recommender systems and contribute to fresh challenges in both academia and industry. In the long term, this research direction has the potential to revolutionize the traditional recommender paradigms and foster the development of next-generation recommender systems. |
| title | The 2nd Workshop on Recommendation with Generative Models |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2403.04399 |