Abstract
Sequential recommendation aims to predict a user’s next behavior in near future by using the user’s most recent behaviors. Most of the existing methods always embed a user or an item as a point in a vector space, and then model the user’s recent behaviors as a sequence with a strict order to generate recommendations. However, both the point representation and strict order rule limit the capacity of sequential recommendation models as the diversity and uncertainty of a user’s interests. In this paper, by relaxing the condition that a sequence must follow a strict order, we introduce the box embedding into the sequential recommendation and present a novel model called Box4Rec. Box4Rec embeds a user and the user’s historical items as boxes instead of points to model the user’s general preference and short-term preference, and then integrates the conjunction and disjunction operations on items to generate flexible recommendation strategies. Experiments on five real-world datasets show the proposed Box4Rec model outperforms the state-of-the-art methods consistently.
Supported by organization Program of Guizhou Provincial Science and Technology Department (No. [2019]2502).
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Deng, K., Huang, J., Qin, J. (2021). Box4Rec: Box Embedding for Sequential Recommendation. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12713. Springer, Cham. https://doi.org/10.1007/978-3-030-75765-6_43
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