A Model-based Multi-Agent Personalized Short-Video Recommender System
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913339995586560 |
|---|---|
| author | Zhou, Peilun Xu, Xiaoxiao Hu, Lantao Li, Han Jiang, Peng |
| author_facet | Zhou, Peilun Xu, Xiaoxiao Hu, Lantao Li, Han Jiang, Peng |
| contents | Recommender selects and presents top-K items to the user at each online request, and a recommendation session consists of several sequential requests. Formulating a recommendation session as a Markov decision process and solving it by reinforcement learning (RL) framework has attracted increasing attention from both academic and industry communities. In this paper, we propose a RL-based industrial short-video recommender ranking framework, which models and maximizes user watch-time in an environment of user multi-aspect preferences by a collaborative multi-agent formulization. Moreover, our proposed framework adopts a model-based learning approach to alleviate the sample selection bias which is a crucial but intractable problem in industrial recommender system. Extensive offline evaluations and live experiments confirm the effectiveness of our proposed method over alternatives. Our proposed approach has been deployed in our real large-scale short-video sharing platform, successfully serving over hundreds of millions users. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01847 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Model-based Multi-Agent Personalized Short-Video Recommender System Zhou, Peilun Xu, Xiaoxiao Hu, Lantao Li, Han Jiang, Peng Information Retrieval Artificial Intelligence Recommender selects and presents top-K items to the user at each online request, and a recommendation session consists of several sequential requests. Formulating a recommendation session as a Markov decision process and solving it by reinforcement learning (RL) framework has attracted increasing attention from both academic and industry communities. In this paper, we propose a RL-based industrial short-video recommender ranking framework, which models and maximizes user watch-time in an environment of user multi-aspect preferences by a collaborative multi-agent formulization. Moreover, our proposed framework adopts a model-based learning approach to alleviate the sample selection bias which is a crucial but intractable problem in industrial recommender system. Extensive offline evaluations and live experiments confirm the effectiveness of our proposed method over alternatives. Our proposed approach has been deployed in our real large-scale short-video sharing platform, successfully serving over hundreds of millions users. |
| title | A Model-based Multi-Agent Personalized Short-Video Recommender System |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2405.01847 |