MALP: A More Effective MetaPaths Based Link Prediction Method in Partially Aligned Heterogeneous Social Networks In general, online social networks include different types of nodes and edges, which means that online social networks are a type of heterogeneous information network. Link prediction is a very important research problem in heterogeneous social networks. The solution to this problem is generally to predict the possibility of a link between two nodes by extracting the characteristics of the nodes in the network. However, the information provided by a single network may not be sufficient, so useful information can be passed from other networks to assist in link prediction in the target network. This is called a partially aligned heterogeneous social network link prediction problem. In this paper, a method, called Metapath and AUC optimization based Link Predictor(MALP), is proposed to predict the social links in the partially aligned social networks at the same time with a semisupervised AUC optimization technology. Experimental results on real social network data show that our approach exhibits better predictive performance than other stateoftheart methods.

Recommendation generation using personalized weight of metapaths in heterogeneous information networks Abstract   In todayu0027s era of electronic markets with information overload, generating personalized recommendations for ecommerce users is a challenging and interesting problem. Recommending topN items of interest to ecommerce users is more challenging using binary implicit feedback. The training data is usually highly sparse and has binary values capturing a useru0027s action or inaction. Due to the sparseness of data and lack of explicit user preferences, neighborhoodbased and modelbased approaches may not be effective to generate accurate recommendations. Of late, networkbased item recommendation methods, which utilize item related metainformation, have started getting attention. In this work, we propose a heterogeneous information networkbased recommendation model called HeteroPRS for personalized topN recommendations using binary implicit feedback. To utilize the potential of metainformation related to items, we use the concept of metapath. To improve the effectiveness of the recommendations, the popularity of items and interest of users are leveraged simultaneously. Personalized weight learning of various metapaths in the network is performed to determine the intrinsic interests of users from the binary implicit feedback. The proposed model is experimentally evaluated and compared with various recommendation techniques for implicit feedback using realworld datasets, and the results show the effectiveness of the proposed model.