Multi-behavior Session Based Recommendations (MBSBRs) have achieved remarkable results due to considering behavioral heterogeneity in sessions. Yet most existing works only consider binary or continuous behavior dependencies and aim to predict the next item under the target behavior, neglecting users' inherent behavior habits, resulting in learning inaccurate intentions. To tackle the above issues, we propose a novelBehaviorHabits Enhanced Intention Learning framework forSessionBasedRecommendation (BHSBR). Specifically, we focus on the next item recommendation and design a global item transition graph to learn the behavior-aware semantic relationships between items, in order to mine the underlying similarity between items beyond the session. In addition, we construct a hypergraph to extract the diverse behavior habits of users and break through the limitations of temporal relationships in the session. Compared to the existing works, our behavior habit learning method learns behavior dependencies at the user level, which could capture the user's more accurate long-term intentions and reduce the impact of noise behaviors. Extensive experiments on three datasets demonstrate that the performance of our proposedBHSBRis superior to SOTA. Further ablation experiments fully illustrate the effectiveness of our various modules.

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.

Conversational Recommender Systems (CRSs) interact with users through natural language to provide recommendations and generate responses. Due to limited information in conversation, existing works utilize KGs or reviews to improve CRS. Despite achievements, they overlook co-occurrence relations which have shown effectiveness in collaborative filtering systems. In this work, we first propose a novel framework named CoCRS , aiming to incorporate Co-occurrences into the Review-based Conversation Recommendation Systems . In CoCRS, we mine co-occurrences from two aspects: (1) item and entity , (2) user and item . For the first one, we extract entities from redundant review texts by KG and construct a relation-aware item-entity heterogeneous graph. In the second aspect, we analyze review sentiments and construct a sentiment-aware user-item bipartite graph. We encode two graphs to obtain user and entity embeddings. Since users in CRS are anonymous, we generate a virtual similar user representation to match reviews with users. Besides, we capture time-aware preference representation from two-time dimensions. Finally, we generate word-level user representation with word-oriented KG and model user preference by integrating the above representations. Extensive experiments demonstrate that CoCRS outperforms baselines and the cold-start experiment highlights its robustness. The Large Language Model (LLM) experiment illustrates the significant role of co-occurrence relationships in LLM-based CRS. Our code are available at https://github.com/Qin-lab-code/CoCRS .

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.