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• W2980778868 abstract "Predicting what items a user will consume in the next time (i.e., next-item recommendation) is a crucial task for recommender systems. While the factorization method is a popular choice in recommendation, several recent efforts have shown that the inner product does not satisfy the triangle inequality, which may hurt the model's generalization ability. TransRec is a promising method to overcome this issue, which learns a distance metric to predict the strength of user-item interactions. Nevertheless, such method only uses the latest consumed item to model a user's short-term preference, which is insufficient for modeling fidelity. In this article, we propose a simple yet effective method named attentive translation model, to explicitly exploit high-order sequential information for next-item recommendation. Specifically, we construct a user-specific translation vector by accounting for multiple recent items, which encode more information about a user's short-term preference than the latest item. To aggregate multiple items into one representation, we devise a position-aware attention mechanism, learning different weights on items at different orders in a personalized way. Extensive experiments on four real-world datasets show that our method significantly outperforms several state-of-the-art methods." @default.
• W2980778868 created "2019-10-25" @default.
• W2980778868 creator A5005167596 @default.
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• W2980778868 date "2020-03-01" @default.
• W2980778868 modified "2023-10-17" @default.
• W2980778868 title "ATM: An Attentive Translation Model for Next-Item Recommendation" @default.
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• W2980778868 doi "https://doi.org/10.1109/tii.2019.2947174" @default.
• W2980778868 hasPublicationYear "2020" @default.
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