Heterogenous Information Network Embedding Based CrossDomain Recommendation System The data sparsity or the cold start is a general bottleneck of recommendation service systems. Two types of stateofart schemes address this issue via enriching knowledge. Crossdomain recommendation schemes transfer knowledge from another dense domain, but they only explore a single kind of shared knowledge. Recommendation schemes built on heterogeneous information networks (HIN) utilize knowledge implied in metapaths connecting various objects, these methods, however, investigate metapaths in a single domain. To improve recommendation performance in a highly sparse scenario, we propose a HIN Embedding based Crossdomain Recommendation (HecRec) framework, which exploits crossdomain information by establishing metapath based HIN embeddings in both the source and the target domain and conducts personalized recommendation by integrating the obtained HIN embeddings with a rating predictor. To make the best use of crossdomain information and avoid the knowledge confliction between knowledge from different metapaths observed in realsystem datasets, we adopt a concept of overpass bridge to integrate the HIN embeddings drawn via different metapaths. Experiments on two public datasets, i.e., MovieLens and LibraryThing, demonstrate the capacity of HecRec on addressing data sparseness. The recommendation performance of HecRec is improved up to 6.9% in MAE and 5.5% in RMSE compared with the stateofart schemes in the coldest cases.

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.