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Dual contrastive learning with behavior pattern modeling for session-based recommendation
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AI
DOI：10.1016/j.knosys.2026.115281.png)
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Abstract
En 中文
Session-based recommendation (SBR) that provides personalized predictions based on anonymous users’ short-term clicks has recently gained widespread attention. Nowadays, numerous SBR models overlook the joint extraction of explicit and implicit feedback, leading to biases in user behavior modeling. Meanwhile, most methods fail to fully leverage the latent information in repeated items and click orders within sessions, exacerbating the negative effects of data sparsity in SBR. To address the above issues, we propose the Dual Contrastive Learning with Behavior Pattern Modeling (DCL-BPM) method, which maximizes the use of short-term session information while extracting long-range user dependencies for recommendation. Specifically, we first employ GGNN and E-GNN to extract implicit and explicit feedback separately, effectively combining them to construct an accurate dynamic user profile. We then add the filtered session embeddings to prevent data loss caused by gradient mismatch. To better capture user preferences, we design a Dual Contrastive Loss (DCL) framework that constructs negative samples through deduplication and random reshuffling, highlighting the critical role of item frequency and click orders in positive samples during training. DCL is not limited by the network architecture, making it easily adaptable to diverse scenarios in SBR. Extensive experiments on three representative datasets demonstrate the effectiveness of our model and its practical value in real-world applications.
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