Match4Rec: A Novel Recommendation Algorithm Based on Bidirectional Encoder Representation with the Matching Task

Abstract

Characterizing users' interests accurately plays a significant role in an effective recommender system. The sequential recommender system can learn powerful hidden representations of users from successive user-item interactions and dynamic users' preferences. To analyze such sequential data, conventional methods mainly include Markov Chains (MCs) and Recurrent Neural Networks (RNNs). Recently, the use of self-attention mechanisms and bi-directional architectures have gained much attention. However, there still exists a major limitation in previous works that they only model the user's main purposes in the behavioral sequences separately and locally, and they lack the global representation of the user's whole sequential behavior. To address this limitation, we propose a novel bidirectional sequential recommendation algorithm that integrates the user's local purposes with the global preference by additive supervision of the matching task. We combine the mask task with the matching task in the training process of the bidirectional encoder. A new sample production method is also introduced to alleviate the effect of mask noise. Our proposed model can not only learn bidirectional semantics from users' behavioral sequences but also explicitly produces user representations to capture user's global preference. Extensive empirical studies demonstrate our approach considerably outperforms various state-of-the-art models.

arXiv commentAccepted by ICONIP2020

References in corpus (5)

• Distributed Representations of Sentences and Documents
• Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
• Sequential Recommender Systems: Challenges, Progress and Prospects
• BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
• Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation