Abstract
Session-based Recommender Systems (SBRSs) aim to recommend the next item to users based on their historical interactions with items within or between sessions. A session is constituted by a sequence of interactions between the user and items within a continuous period. Existing SBRSs often focus on modeling co-occurrence-based inter-item transitions within or between sessions only. They generally overlook intrinsic inter-item semantic relations. Specifically, in practice, many items are substitutable or complementary to each other. Such relations provide significant signals to guide user interaction behaviours as well as the next-item recommendations. Moreover, existing works overlook the fact that user behaviours are driven simultaneously by both user intent and item attributes, failing to consider the implicit item characteristics embedded within. Such practice leads to entangled user intent and latent item characteristics, bringing unnecessary interference between these two aspects, impeding accurate modeling of each aspect, ultimately significantly impeding recommendation performance. To bridge these gaps, we propose a novel framework called Semantic relation guided dual-view Contrastive Learning for Session-based Recommendations (SCL-SR). SCL-SR introduces a novel semantic relation-guided contrastive learning module to capture additional supervision signals from both user intent view and item attribute view to guide the next-item prediction better. Then, we propose a novel intent-attribute disentangler to effectively mitigate the interference between user intent and latent item characteristics for further improving the recommendation performance. Extensive experiments on three real-world datasets demonstrate the significant superiority of SCL-SR over the state-of-the-art approaches, including achieving substantial improvements ranging from 7.10% to 12.82% on the Tmall dataset1.