Sequential recommendation is essential in modern recommender systems, focusing on effectively extracting and expressing user representations. Most existing methods rely on deep neural networks that employ a single vector for user interests, neglecting their multi-dimensional nature. This limitation hampers the accurate representation of user preferences. Meanwhile, with the development of deep learning and large models, the growing complexity of deep learning models increases hardware and training costs. In this paper, SCAD (Advancing Sequence Augmentation with Coupling Attention Dynamic Routing), a neighbor-based sequential recommender model, is proposed to tackle these challenges, and it can be regarded as a lightweight recommender system model. SCAD features three main components: the Neighbor Interest Activation (NIA) module, which enhances user representation by exploring similar users; the Coupling Attention Dynamic Routing (CAD) module, which uses a Capsule Network to determine the optimal representation strategy; and the “Interest Merge” module, which integrates single- and multi-interest information for improved preference extraction. Generally speaking, SCAD is superior to most existing methods in constructing user interests, and significantly improves the accuracy of recommendation. Extensive experiments on two real-world benchmarks demonstrate that SCAD outperforms existing top-performance methods in terms of recommendation accuracy.

Previous user-item interaction graphs have typically focused on simple interaction between users and items, failing to identify the important effects of user's intents in the interaction. While recent studies have ventured into exploring intent relationships between users and items for modeling, they predominantly emphasize user preferences manifesting in the interaction, overlooking knowledge-driven insight, thereby limiting the interpretability of intent. In this paper, we utilize the rich interpretable knowledge information in the knowledge graph to design a novel dual-level intents modeling framework called DIM. DIM aims to mine user's true intents, which usually include user popularity preference and personalized preference. Therefore, we extract both the popular and personalized user preferences from attribute tuples within the knowledge graph at the global and local levels, respectively. Experimental results on three datasets demonstrate the superiority of DIM over various state-of-the-art approaches.

Recommendation systems have shifted from personalization for individual users to consensus for groups as a result of people’s growing tendency to join groups to participate in various everyday activities, like family meals and workplace reunions. This is because social networks have made it easier for people to participate in these kinds of events. Group recommendation is the process of suggesting items to groups. To derive group preferences, the majority of current approaches combine the individual preferences of group members utilizing heuristic or attention mechanism-based techniques. These approaches, however, have three issues. First, these approaches ignore the complex high-order interactions that occur both inside and outside of groups, just modeling the preferences of individual groups of users. Second, a group’s ultimate decision is not always determined by the members’ preferences. Nevertheless, current approaches are not adequate to represent such preferences across groups. Last, data sparsity affects group recommendations due to the sparsity of group–item interactions. To overcome the aforementioned constraints, we propose employing hypergraph convolutional networks for group recommendation. Specifically, our design aims to achieve excellent group preferences by establishing a high-order preference extraction view represented by the hypergraph, a consistent preference extraction view represented by the overlap graph, and a conventional preference extraction view represented by the bipartite graph. The linkages between the three various views are then established by using cross-view contrastive learning, and the information between different views can be complementary, thereby improving each other. Comprehensive experiments on three publicly available datasets show that our method performs better than the state-of-the-art baseline.