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Article overview
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Learning User Representations with Hypercuboids for Recommender Systems | | Shuai Zhang
; Huoyu Liu
; Aston Zhang
; Yue Hu
; Ce Zhang
; Yumeng Li
; Tanchao Zhu
; Shaojian He
; Wenwu Ou
; | | Date: |
Wed, 11 Nov 2020 12:50:00 GMT (18040kb,D) | | Abstract: | Modeling user interests is crucial in real-world recommender systems. In this
paper, we present a new user interest representation model for personalized
recommendation. Specifically, the key novelty behind our model is that it
explicitly models user interests as a hypercuboid instead of a point in the
space. In our approach, the recommendation score is learned by calculating a
compositional distance between the user hypercuboid and the item. This helps to
alleviate the potential geometric inflexibility of existing collaborative
filtering approaches, enabling a greater extent of modeling capability.
Furthermore, we present two variants of hypercuboids to enhance the capability
in capturing the diversities of user interests. A neural architecture is also
proposed to facilitate user hypercuboid learning by capturing the activity
sequences (e.g., buy and rate) of users. We demonstrate the effectiveness of
our proposed model via extensive experiments on both public and commercial
datasets. Empirical results show that our approach achieves very promising
results, outperforming existing state-of-the-art. | | Source: | arXiv, 2011.05742 | | Services: | Forum | Review | PDF | Favorites | |
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