Orthogonal Hyper-category Guided Multi-interest Elicitation for Micro-video Matching
<|reference_start|>Orthogonal Hyper-category Guided Multi-interest Elicitation for Micro-video Matching: 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.<|reference_end|>
@article{li2024orthogonal,
    title={Orthogonal Hyper-category Guided Multi-interest Elicitation for
  Micro-video Matching},
    author={Beibei Li, Beihong Jin, Yisong Yu, Yiyuan Zheng, Jiageng Song, Wei
  Zhuo, Tao Xiang},
    journal={arXiv preprint arXiv:2407.14741},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14741},
    primaryClass={cs.IR cs.AI}
}