Session-based recommendation (SBR) aims to predict upcoming user choices based on brief interaction histories. Over the past few years, graph neural networks (GNNs) have become a powerful tool for capturing intricate item relationships and delivering effective recommendations. Existing works use the sequential interactions within all sessions to construct graphs and provide self-supervised signals. Although some progresses have been made, we argue that merely relying on the transitions pattern fail to fully mine the complex information among items and lead to limited item representations. This article introduces a semantic information and intention enhanced SBR paradigm, which is called SISR. Our SISR leverages not only the sequential order of items but also the session intentions and semantic neighbors. Specifically, we begin by constructing a global item transition graph to enhance the GNN-based SBR with insights from items across all sessions. Then, the clustering mechanism is applied to obtain latent semantic prototypes of items and further extract the intention representations of sessions. Finally, we propose two contrastive learning component to distill the self-supervised signals for the item representation learning, so as to alleviate the data-sparsity phenomenon and augment the item recommendation component ratio. Comprehensive testing on three real-world datasets against various leading models highlights the advantages of our SISR paradigm.

In recent years, social recommendation systems have emerged as a pivotal technology for enhancing recommendation accuracy by leveraging user social homophily and influence. Although many works have been devoted to this area, existing works still struggle to extract the beneficial structural information from social relationships that is beneficial for recommendations and neglect the inherent popularity bias in the social networks, which leads to suboptimal recommendation performances. To address these challenges, we propose a novel framework termed Causal Disentanglement-Enhanced Diffusion Denoising for Social Recommendation (CaDDiSR). This framework first employs causal graphs to disentangle the complexities of social relationships, generating user representations with high-order structures, which are subsequently used as inputs to a diffusion process to effectively denoise social networks and retain social signals beneficial for recommendation tasks. Furthermore, the framework integrates a bidirectional knowledge distillation mechanism, which balances user representations between social and recommendation contexts, thereby facilitating the effective fusion of their respective advantages while simultaneously mitigating noise interference and enhancing overall system performance. Finally, cross-domain contrastive learning is utilized to optimize user and item representations, ensuring consistency in recommendation performance across diverse scenarios. Experimental results on multiple real-world datasets demonstrate that CaDDiSR significantly outperforms existing baseline models, substantiating its superior performance.

Session-based recommendation (SBR) systems have increasingly focused on hypergraph-based approaches due to their potent capability in capturing high-order item relationships. Typically, existing approaches rely on sequential item relations to manually construct fixed hypergraphs. However, this methodology neglects the multiple relations inherent in the original sequences, thereby impeding the hypergraph’s precision in discerning user preferences. Furthermore, the rigidity of fixed hypergraph structures tends to emphasize explicit relationships, ignoring the latent implicit patterns. In light of this, we present a novel Multi-relation enhanced Dynamic HyperGraph (MDHG) learning framework for session-based recommendation, to model intricate and variable item relations. Initially, we establish three distinct relation graphs which capture separate user behavior patterns to extract personalized interest preferences under differentiated intentions. Subsequently, we propose an enhanced dynamic hypergraph paradigm that adaptively generates hypergraph structures based on prior relation graph, thereby reinforcing and unveiling implicit connectivity relations in a layer-aware manner. Finally, to mitigate the noise among diverse relations, we introduce the maximum mutual information auxiliary task and employ the attention mechanism as a cross-relation aggregator. Extensive experiments on various real-world datasets verify the superiority of our MDHG model. Our code is publicly available at https://github.com/Qin-lab-code/MDHG .

Sequential recommendation (SR) is widely deployed in e-commerce platforms, streaming services, etc., revealing significant potential to enhance user experience. The core of SR lies in exploring the sequential relationships in historical user-item interactions. However, existing methods often overlook two critical factors: irregular user interests between interactions and highly uneven item distributions over time. The former factor implies that actual user preferences are not always continuous, and long-term historical interactions may not be relevant to current purchasing behavior. Therefore, relying only on these historical interactions for recommendations may result in a lack of user interest at the target time. The latter factor, characterized by peaks and valleys in interaction frequency, may result from seasonal trends, special events, or promotions. These externally driven distributions may not align with individual user interests, leading to inaccurate recommendations. To address these deficiencies, we propose TGODE to both enhance and capture the long-term historical interactions. Specifically, we first construct the user time graph and item evolution graph, which utilize user personalized preferences and global item distribution information, respectively. To tackle the temporal sparsity caused by irregular user interactions, we design a time-guided diffusion generator to automatically obtain an augmented time-aware user graph. Additionally, we devise a user interest truncation factor to efficiently identify sparse time intervals and achieve balanced preference inference. After that, the augmented user graph and item graph are fed into a generalized graph neural ordinary differential equation (ODE) to align with the evolution of user preferences and item distributions. This allows two patterns of information evolution to be matched over time. Experimental results demonstrate that TGODE outperforms baseline methods across five datasets, with improvements ranging from 10% to 46%. The code is available at https://github.com/Qin-lab-code/TGODE.

Diversified recommendation systems have gained increasing popularity in recent years. Nowadays, the emerged graph neural networks (GNNs) have been used to improve the diversity performance. Although some progresses have been made, existing works purely focus on the user–item interactions and overlook the category information, which limits the capability to capture complex diversification among users or items and leads to poor performance. In this article, our target is to integrate full category information into user and item embeddings. To this end, we propose a diversified GNN-based recommendation systems diversified graph recommendation with contrastive learning (DCL). Specifically, we design three key components in our model: 1) the user–item interaction with category-related sampling enhances the interaction of unpopular items; 2) contrastive learning between users and categories shortens the distance of representations between users and their uninteracted categories; and 3) contrastive learning between items and categories diverges the distance of representations between items and their corresponding categories. By applying these three modules, we build a multitask training framework to achieve a balance between accuracy and diversity. Experiments on real-world datasets show that our proposed DCL achieves optimal diversity while paying a little price for accuracy.