Orthogonal Hyper-category Guided Multi-interest Elicitation for Micro-video Matching
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914879671107584 |
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| author | Li, Beibei Jin, Beihong Yu, Yisong Zheng, Yiyuan Song, Jiageng Zhuo, Wei Xiang, Tao |
| author_facet | Li, Beibei Jin, Beihong Yu, Yisong Zheng, Yiyuan Song, Jiageng Zhuo, Wei Xiang, Tao |
| contents | Watching micro-videos is becoming a part of public daily life. Usually, user watching behaviors are thought to be rooted in their multiple different interests. In the paper, we propose a model named OPAL for micro-video matching, which elicits a user's multiple heterogeneous interests by disentangling multiple soft and hard interest embeddings from user interactions. Moreover, OPAL employs a two-stage training strategy, in which the pre-train is to generate soft interests from historical interactions under the guidance of orthogonal hyper-categories of micro-videos and the fine-tune is to reinforce the degree of disentanglement among the interests and learn the temporal evolution of each interest of each user. We conduct extensive experiments on two real-world datasets. The results show that OPAL not only returns diversified micro-videos but also outperforms six state-of-the-art models in terms of recall and hit rate. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14741 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Orthogonal Hyper-category Guided Multi-interest Elicitation for Micro-video Matching Li, Beibei Jin, Beihong Yu, Yisong Zheng, Yiyuan Song, Jiageng Zhuo, Wei Xiang, Tao Information Retrieval Artificial Intelligence Watching micro-videos is becoming a part of public daily life. Usually, user watching behaviors are thought to be rooted in their multiple different interests. In the paper, we propose a model named OPAL for micro-video matching, which elicits a user's multiple heterogeneous interests by disentangling multiple soft and hard interest embeddings from user interactions. Moreover, OPAL employs a two-stage training strategy, in which the pre-train is to generate soft interests from historical interactions under the guidance of orthogonal hyper-categories of micro-videos and the fine-tune is to reinforce the degree of disentanglement among the interests and learn the temporal evolution of each interest of each user. We conduct extensive experiments on two real-world datasets. The results show that OPAL not only returns diversified micro-videos but also outperforms six state-of-the-art models in terms of recall and hit rate. |
| title | Orthogonal Hyper-category Guided Multi-interest Elicitation for Micro-video Matching |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2407.14741 |