Multi-interest sequential recommendations leverage users’ historical behavior to provide recommendations that match multiple interests. Most of these methods have not fully extracted higher-order information hidden in users’ interactions and have overlooked the multiple features of items. To this end, this paper proposes a multi-interest model called “multi-interest sequential recommendation with simplified graph convolution and item multi-features (SGCMF)”. Firstly, a simplified graph convolution module is designed based on bipartite graphs, which utilizes mean pooling to aggregate neighboring information and employs a feedforward neural network (FNN) for nonlinear transformations and combinations. This method reduces redundant information and captures higher-order relationships, thereby simplifying the complexity of modeling high-order interactions and improving prediction accuracy. Secondly, an item multi-feature extraction module is proposed, which represents item features with multiple vectors, and analyzes each feature from multiple perspectives while preserving important relationships between features. The model correlates multiple features of the item with user interests, thereby achieving a fine-grained analysis of user interests. Extensive experiments are conducted on five real-world scenarios, and the results are compared with state-of-the-art methods. The experimental results show that SGCMF outperforms other baselines.

Mapping-based cross-domain recommendation (CDR) can effectively tackle the cold-start problem in traditional recommender systems.However, existing mapping-based CDR methods ignore datasparse users in the source domain, which may impact the transfer efficiency of their preferences.To this end, this paper proposes a novel method named Neighbor Interaction-based Personalized Transfer for Cross-Domain Recommendation (NIPT-CDR).This proposed method mainly contains two modules: (i) an intra-domain item supplementing module and (ii) a personalised feature transfer module.The first module introduces neighbour interactions to supplement the potential missing preferences for each source domain user, particularly for those with limited observed interactions.This approach comprehensively captures the preferences of all users.The second module develops an attention mechanism to guide the knowledge transfer process selectively.Moreover, a meta-network based on users' transferable features is trained to construct personalised mapping functions for each user.The experimental results on two real-world datasets show that the proposed NIPT-CDR method achieves significant performance improvements compared to seven baseline models.The proposed model can provide more accurate and personalised recommendation services for cold-start users.