CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space
This work addresses a specific problem in recommendation systems for anonymous users, offering an incremental improvement over existing methods.
The paper tackles the inconsistent prediction issue in session-based recommendation by proposing CORE, a framework that unifies the representation space for sessions and items, achieving state-of-the-art results on five public datasets.
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: https://github.com/RUCAIBox/CORE.