A CommunityEnhanced Retrieval Model for TextRich Heterogeneous Information Networks In recent years, we have witnessed a large increase in the number of available textrich heterogeneous networks, in which text documents, users and other objects are interconnected in various ways. Examples include social networks, bibliographic information networks, and collaboration networks. Textrich heterogeneous networks contain text nodes (e.g., user comments in social networks; scientific publications in bibliographic networks), nontext nodes (e.g., users and companies in social networks), as well as rich relationships between different types of nodes. An important task, which is very useful on its own right, as well as often serves as a preprocessing to mining and analytical tasks, is identifying relevant text information from the heterogeneous information networks given user queries. Unlike Web search and many related problems, the effects of the underlying semantically meaningful patterns in heterogeneous networks play an important role in determining relevant answers for user search queries. For example, there are several structural patterns we can explore to more effectively identify relevant text nodes for a given query. The relevant nodes are likely to come from the same usertopic communities as the query. In addition, the relevant nodes on the same query topic tend to form tight local relationships with each other in the same usertopic community. In this paper, we present a unified and principled framework that effectively integrates network community and local relationship detection with retrieval model construction in a mutually enhancing manner, which leads to a communityenhanced retrieval model for textrich heterogeneous information networks. Experimental results show that this new model significantly outperforms stateoftheart baselines.

News Recommendation Model Based on Improved Label Propagation Algorithm Facing the everexpanding scale of news information, how to filter and redundant information in complex and diverse data to accurately recommend information to users has become an important challenge. News recommendations have become a powerful tool for dealing with information overload. Scholars have done a lot of research on personalized news recommendation. However, due to the sparseness of user data, the traditional collaborative filtering algorithm is not only too timeconsuming but also less accurate. Therefore, aiming at the characteristics of academic social networks, we propose a news recommendation model based on community detection, which builds a friend relationship community by using the users friend relationship and coherent neighborhood propinquity algorithm, and then integrates the collaborative filtering algorithm to recommend news to users. Through verification on the dataset of the academic social network SCHOLAT, we can prove that the recommendation model can achieve good accuracy while improving recommendation efficiency.