Auto Encoding Neural Process for Multi-interest Recommendation
Yiheng Jiang, Yuanbo Xu, Yongjian Yang, Funing Yang, Pengyang Wang, Chaozhuo Li
Abstract
Multi-interest recommendation constantly aspires to an oracle individual preference modeling approach, that satisfies the diverse and dynamic properties. Fueled by the deep learning technology, existing neural network (NN)-based recommender systems employ single-point or multi-point interest representation strategy to realize preference modeling,and boost the recommendation performance with a remarkable margin. However, as parameterized approximate functions, NN-based methods remain deficiencies with respect to the adaptability towards distinctive preference patterns cross different users and the calibration over the individual current intent. In this paper, we revisit multi-interest recommendation with the lens of stochastic process and Bayesian inference. Specifically, we propose to learn a distribution over functions to depict the individual diverse preferences rather than a unified function to approximate preference. Subsequently, the recommendation is encouraged with the uncertainty estimation which conforms to the dynamic shifting intent. Along these lines, we establish the connection between multi-interest recommendation and neural processes by proposing NP-Rec, which realizes the flexible multiple interests modeling and uncertainty estimation, simultaneously. Empirical study on 4 real world datasets demonstrates that our NP-Rec attains superior recommendation performances to several state-of-the-art baselines, where the average improvement achieves up to 13.94%.
BibTeX
@article{Jiang_Xu_Yang_Yang_Wang_Li_2025, title={Auto Encoding Neural Process for Multi-interest Recommendation}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/33293}, DOI={10.1609/aaai.v39i11.33293}, abstractNote={Multi-interest recommendation constantly aspires to an oracle individual preference modeling approach, that satisfies the diverse and dynamic properties. Fueled by the deep learning technology, existing neural network (NN)-based recommender systems employ single-point or multi-point interest representation strategy to realize preference modeling,and boost the recommendation performance with a remarkable margin. However, as parameterized approximate functions, NN-based methods remain deficiencies with respect to the adaptability towards distinctive preference patterns cross different users and the calibration over the individual current intent. In this paper, we revisit multi-interest recommendation with the lens of stochastic process and Bayesian inference. Specifically, we propose to learn a distribution over functions to depict the individual diverse preferences rather than a unified function to approximate preference. Subsequently, the recommendation is encouraged with the uncertainty estimation which conforms to the dynamic shifting intent. Along these lines, we establish the connection between multi-interest recommendation and neural processes by proposing NP-Rec, which realizes the flexible multiple interests modeling and uncertainty estimation, simultaneously. Empirical study on 4 real world datasets demonstrates that our NP-Rec attains superior recommendation performances to several state-of-the-art baselines, where the average improvement achieves up to 13.94%.}, number={11}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Jiang, Yiheng and Xu, Yuanbo and Yang, Yongjian and Yang, Funing and Wang, Pengyang and Li, Chaozhuo}, year={2025}, month={Apr.}, pages={11879-11887} }