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 behaviors as well as the next-item recommendations. Moreover, existing works overlook the fact that user behaviors 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 S emantic relation guided dual-view C ontrastive L earning for S ession-based R ecommendations (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 dataset. Our source code and datasets are available at https://github.com/Nishikata97/SCL-SR .

Next basket recommender systems (NBRs) aim to recommend a user’s next (shopping) basket of items via modeling the user’s preferences towards items based on the user’s purchase history, usually a sequence of historical baskets. Due to its wide applicability in the real-world E-commerce industry, the studies NBR have attracted increasing attention in recent years. NBRs have been widely studied and much progress has been achieved in this area with a variety of NBR approaches having been proposed. However, an important issue is that there is a lack of a systematic and unified evaluation over the various NBR approaches. Different studies often evaluate NBR approaches on different datasets, under different experimental settings, making it hard to fairly and effectively compare the performance of different NBR approaches. To bridge this gap, in this work, we conduct a systematical empirical study in NBR area. Specifically, we review the representative work in NBR and analyze their cons and pros. Then, we run the selected NBR algorithms on the same datasets, under the same experimental setting and evaluate their performances using the same measurements. This provides a unified framework to fairly compare different NBR approaches. We hope this study can provide a valuable reference for the future research in this vibrant area.

Session-based recommendations (SBR) play an important role in many real-world applications, such as e-commerce and media streaming. To perform accurate session-based recommendations, it is crucial to capture both sequential dependencies over a sequence of adjacent items and complex item transitions over a set of items within sessions. Note that item transitions are not necessarily dependent on sequential dependencies, e.g., the transition from one item to the other distant item in a session is often not sequential. However, almost all the existing session-based recommender systems (SBRS) fail to consider both kinds of information, which leads to their limited performance improvement. Aiming at this deficiency, we propose a novel sequential dependency enhanced graph neural network (SDE-GNN) to capture both sequential dependencies and item transition relations over items within sessions for more accurate next-item recommendations. Specifically, we first devise a sequential dependency learning module to capture the sequential dependencies over a sequence of adjacent items in each session. Then, we propose an item transition learning module to capture complex transitions between items. In the module, a novel residual gate and a specialized attention mechanism are integrated into gate-GNN to build an attention augmented GNN, called AU-GNN. Finally, we devise a gated fusion component to combine the learned sequential dependencies and item transitions together in preparation for the subsequent next-item recommendations. Exhaustive experiments on two public real-world data sets demonstrate the superiority of SDE-GNN over the state-of-the-art methods.