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CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space

2022-04-23 13:20:31
Yupeng Hou, Binbin Hu, Zhiqiang Zhang, Wayne Xin Zhao

Abstract

Session-based Recommendation (SBR) refers to the task of predicting the next item based on short-term user behaviors within an anonymous session. However, session embedding learned by a non-linear encoder is usually not in the same representation space as item embeddings, resulting in the inconsistent prediction issue while recommending items. To address this issue, we propose a simple and effective framework named CORE, which can unify the representation space for both the encoding and decoding processes. Firstly, we design a representation-consistent encoder that takes the linear combination of input item embeddings as session embedding, guaranteeing that sessions and items are in the same representation space. Besides, we propose a robust distance measuring method to prevent overfitting of embeddings in the consistent representation space. Extensive experiments conducted on five public real-world datasets demonstrate the effectiveness and efficiency of the proposed method. The code is available at: this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2204.11067

PDF

https://arxiv.org/pdf/2204.11067.pdf


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