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arXiv · 1905.00453

Signed Distance-based Deep Memory Recommender

Thanh Tran·Xinyue Liu·Kyumin Lee·Xiangnan Kong
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Abstract

Personalized recommendation algorithms learn a user's preference for an item by measuring a distance/similarity between them. However, some of the existing recommendation models (e.g., matrix factorization) assume a linear relationship between the user and item. This approach limits the capacity of recommender systems, since the interactions between users and items in real-world applications are much more complex than the linear relationship. To overcome this limitation, in this paper, we design and propose a deep learning framework called Signed Distance-based Deep Memory Recommender, which captures non-linear relationships between users and items explicitly and implicitly, and work well in both general recommendation task and shopping basket-based recommendation task. Through an extensive empirical study on six real-world datasets in the two recommendation tasks, our proposed approach achieved significant improvement over ten state-of-the-art recommendation models.

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Thanh Tran, Xinyue Liu, Kyumin Lee, Xiangnan Kong. 2019-05-01. Signed Distance-based Deep Memory Recommender. https://doi.org/10.1145/3308558.3313460

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