Publication | Closed Access
Recommendation in heterogeneous information networks with implicit user feedback
205
Citations
10
References
2013
Year
Unknown Venue
EngineeringNetwork AnalysisSocial NetworkLink PredictionEntity Recommendation ProblemComputational Social ScienceInformation RetrievalData ScienceNews RecommendationSocial Network AnalysisImplicit User FeedbackRecommendation QualityKnowledge DiscoveryCold-start ProblemInformation Filtering SystemGroup RecommendersNetwork ScienceBusinessCollaborative Filtering
Recent studies suggest that by using additional user or item relationship information when building hybrid recommender systems, the recommendation quality can be largely improved. However, most such studies only consider a single type of relationship, e.g., social network. Notice that in many applications, the recommendation problem exists in an attribute-rich heterogeneous information network environment. In this paper, we study the entity recommendation problem in heterogeneous information networks. We propose to combine various relationship information from the network with user feedback to provide high quality recommendation results.
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