Contrastive learning has demonstrated promising potential in recommender systems. Existing methods typically construct sparser views by randomly perturbing the original interaction graph, as they have no idea about the authentic user preferences. Owing to the sparse nature of recommendation data, this paradigm can only capture insufficient semantic information. To address the issue, we propose InfoDCL, a novel diffusion-based contrastive learning framework for recommendation. Rather than injecting randomly sampled Gaussian noise, we employ a single-step diffusion process that integrates noise with auxiliary semantic information to generate signals and feed them to the standard diffusion process to generate authentic user preferences as contrastive views. Besides, based on a comprehensive analysis of the mutual influence between generation and preference learning in InfoDCL, we build a collaborative training objective strategy to transform the interference between them into mutual collaboration. Additionally, we employ multiple GCN layers only during inference stage to incorporate higher-order co-occurrence information while maintaining training efficiency. Extensive experiments on five real-world datasets demonstrate that InfoDCL significantly outperforms state-of-the-art methods. Our InfoDCL offers an effective solution for enhancing recommendation performance and suggests a novel paradigm for applying diffusion method in contrastive learning frameworks.

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 .

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 .