Multi-behavior recommendation leverages multiple user-item interaction information to alleviate data sparsity. Although different types of user-item interactions are temporally mutually exclusive, the sequence of behavioral interactions consisting of multi-level positive feedback signals contains rich information. However, most existing studies have unilaterally focused on the positive utility of auxiliary behaviors, ignoring multi-level user preference information. Effectively fusing multi-behavioral data and better modeling behavioral dependencies are urgent problems that need to be addressed for multi-behavior recommendation. We propose the p arallel learning of p ositive and n egative interests with an a uxiliary-view r epresentation e nhancement (PPN-ARE) scheme for multi-level user interest learning based on multi-behavioral interaction sequences. Specifically, multi-level positive and negative feedback view chains are constructed from multi-behavioral sequence data to learn multi-level user interests. User preference evolution is simulated during multi-behavior interactions using residual connections, and the shortcomings of the cascading structure used for higher-order graph learning are analytically highlighted. The influence of low-quality embeddings of auxiliary behaviors is filtered, and the learning of target behaviors is optimized by designing a representation enhancement layer. Finally, the model is optimized using a multi-task training framework. The experimental results indicate that PPN-ARE significantly improved over the state-of-the-art (SOTA). The open source code is available at https://github.com/lhybq/PPN-ARE .

Personalized recommendation system (PRS) is an effective tool to automatically extract meaningful information from the big data of the users. Collaborative filtering is one of the most widely used personalized recommendation techniques to recommend the personalized products for users. In this paper, a PRS model based on the support vector machine (SVM) is proposed. The proposed model not only considers the items’ content information, but also the users’ demographic and behavior information to fully capture the users’ interests and preferences. Meanwhile, an improved particle swarm optimization (PSO) algorithm is applied to optimize the SVM’s learning parameters. The efficiency of the proposed method is verified by multiple benchmark datasets.