Modeling User Viewing Flow Using Large Language Models for Article Recommendation
Fuente:
arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909130129670144 |
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| author | Liu, Zhenghao Chen, Zulong Zhang, Moufeng Duan, Shaoyang Wen, Hong Li, Liangyue Li, Nan Gu, Yu Yu, Ge |
| author_facet | Liu, Zhenghao Chen, Zulong Zhang, Moufeng Duan, Shaoyang Wen, Hong Li, Liangyue Li, Nan Gu, Yu Yu, Ge |
| contents | This paper proposes the User Viewing Flow Modeling (SINGLE) method for the article recommendation task, which models the user constant preference and instant interest from user-clicked articles. Specifically, we first employ a user constant viewing flow modeling method to summarize the user's general interest to recommend articles. In this case, we utilize Large Language Models (LLMs) to capture constant user preferences from previously clicked articles, such as skills and positions. Then we design the user instant viewing flow modeling method to build interactions between user-clicked article history and candidate articles. It attentively reads the representations of user-clicked articles and aims to learn the user's different interest views to match the candidate article. Our experimental results on the Alibaba Technology Association (ATA) website show the advantage of SINGLE, achieving a 2.4% improvement over previous baseline models in the online A/B test. Our further analyses illustrate that SINGLE has the ability to build a more tailored recommendation system by mimicking different article viewing behaviors of users and recommending more appropriate and diverse articles to match user interests. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_07619 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Modeling User Viewing Flow Using Large Language Models for Article Recommendation Liu, Zhenghao Chen, Zulong Zhang, Moufeng Duan, Shaoyang Wen, Hong Li, Liangyue Li, Nan Gu, Yu Yu, Ge Information Retrieval Artificial Intelligence This paper proposes the User Viewing Flow Modeling (SINGLE) method for the article recommendation task, which models the user constant preference and instant interest from user-clicked articles. Specifically, we first employ a user constant viewing flow modeling method to summarize the user's general interest to recommend articles. In this case, we utilize Large Language Models (LLMs) to capture constant user preferences from previously clicked articles, such as skills and positions. Then we design the user instant viewing flow modeling method to build interactions between user-clicked article history and candidate articles. It attentively reads the representations of user-clicked articles and aims to learn the user's different interest views to match the candidate article. Our experimental results on the Alibaba Technology Association (ATA) website show the advantage of SINGLE, achieving a 2.4% improvement over previous baseline models in the online A/B test. Our further analyses illustrate that SINGLE has the ability to build a more tailored recommendation system by mimicking different article viewing behaviors of users and recommending more appropriate and diverse articles to match user interests. |
| title | Modeling User Viewing Flow Using Large Language Models for Article Recommendation |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2311.07619 |