LAUSR

Personalized recommendations to cold start user is one of the significant challenges in information filtering systems. Most of the existing systems inherited the idea of collaborative filtering (CF) and avoids the item metadata. For instance, consider a book domain whose metadata are author, publisher, language...etc. The elimination of item metadata is due to the diverse nature of item attributes in a cross-domain environment. Because of this ideology, it is hard for the filtering systems to provide better recommendations to cold start users. Cold start users are people who are new to a domain whose preferences are unknown. A novel model is proposed in this research work called Cross-Domain Semantic Relatedness based Matrix Factorization model (CD-SemMF) resolves the user cold start issue in collaborative filtering recommender system by exploiting Linked Open Data (LOD). “DBpedia” is a widely used LOD resource that contains semantic information about different domains, which is used in this research work to resolve the above-said problem. Here, the metadata available in LOD connects the items preferred by the target user from various domains. The fundamental knowledge graph links items from various domains and also benefits from cross-domain information. “LOD Semantic-Relatedness Measure” a new measure is proposed which calculates the closeness of items across domains. Semantic relatedness is measured instead of similarity in this research because the cross-domain item attributes are diverse. The Alternating Least Square method is applied here to learn the user preferences. The proposed model provides relevant, personalized recommendations for the target new user with the user preferences gained from the source domain and by exploiting item semantic relatedness. Experimental evaluation is done on Facebook and Amazon datasets. It is observed from the result that the proposed CD-SemMF model gives better recommendations in target domain for new users than the baseline methods.