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With the rapid development of the Internet, people are confronted with information overload. Many recommendation methods are designed to solve this problem. The main contributions of recommendation methods proposed in this paper are as follows: (1) An improved collaborative filtering recommendation algorithm based on user clustering is proposed. Clustering is performed according to user similarity based on the user-item rating matrix. So the search space of recommendation algorithm is reduced. (2) Considering the factor that user’s interests may dynamically change over time, a time decay function is introduced. (3) A method of real-time recommendation based on topic for microblogs is designed to realize real-time recommendation effectively by preserving intermediate variables of user similarity. Experiments show that the proposed algorithms have been improved in terms of running time and accuracy.

Ontology user portraits describe the semantic structure of users’ interests. It is very important to study the similar relationship between user portraits to find the communities with overlapping interests. The hierarchical characteristics of user interest can generate multiple similarity relations, which is conducive to the formation of interest clusters. This paper proposed a method of overlapping community detection combining the hierarchical characteristics of user interest and the module distribution entropy of node. First, a hierarchical user interest model was constructed based on the ontology knowledge base to measure the multi-granularity topic similarity of users. Then, a heterogeneous hypergraph was established by using the multi-granularity topic similarity and the following similarity of users to represent the interest network. Based on the mechanism of module distribution entropy of nodes, the community detection algorithm was applied to identify the interested community. The real performance of the proposed algorithm on multiple networks was verified by experiments. The experimental results show that the proposed algorithm is better than the typical overlapping community detection algorithm in terms of accuracy and recall rate.