An Attention-Based Recommender System to Predict Contextual Intent Based on Choice Histories across and within Sessions

Applied Sciences

Shelby McIntyre¹  Zhonghong Ou¹  Meina Song²  Haihong E³  Ruo Huang³ 

[1] Telecommunications, Beijing 100876, China;Leavey School of Business, Santa Clara University, Santa Clara, CA 95053, USA;;School of Computer Science, Beijing University of Posts &

关键词: session-based recommender system;    attention mechanism;    contextual user intent;    recurrent neural network;   

DOI  :  10.3390/app8122426

来源: DOAJ

【 摘 要 】

Recent years have witnessed the growth of recommender systems, with the help of deep learning techniques. Recurrent Neural Networks (RNNs) play an increasingly vital role in various session-based recommender systems, since they use the user’s sequential history to build a comprehensive user profile, which helps improve the recommendation. However, a problem arises regarding how to be aware of the variation in the user’s contextual preference, especially the short-term intent in the near future, and make the best use of it to produce a precise recommendation at the start of a session. We propose a novel approach named Attention-based Short-term and Long-term Model (ASLM), to improve the next-item recommendation, by using an attention-based RNNs integrating both the user’s short-term intent and the long-term preference at the same time with a two-layer network. The experimental study on three real-world datasets and two sub-datasets demonstrates that, compared with other state-of-the-art methods, the proposed approach can significantly improve the next-item recommendation, especially at the start of sessions. As a result, our proposed approach is capable of coping with the cold-start problem at the beginning of each session.

【 授权许可】

Unknown