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Linked Open Data (LOD) is an emerging Web technology to store and publish structured data in the form of interlinked knowledgebases like DBpedia, Freebase, Wikidata, and Yago. It uses structured data from multiple domains, and it can be used to conceptualize a concept of interest. Recently, researchers have shown that incorporating contextual features in recommender systems improves rating prediction accuracy. However, identification of contextual features for building context-aware recommender systems is a major bottleneck. To this end, in this article, we present the development of a context-based recommender system, CRecSys, for item ratings prediction in movie domain. CRecSys extracts item-based contextual features from the underlying dataset and generates an RDF graph to model items and their contextual features for computing context-based items similarity using graph matching techniques and item-based collaborative filtering. It uses LOD and two well-known movie data sources - Rotten Tomatoes and IMDB for item profiling using a dataset of 1300 movies. CRecSys is experimentally evaluated over two movie datasets, one is generated by the authors and second is the MovieLens-1M benchmark dataset. CRecSys is also compared with ten baselines and two state-of-the-art recommendation methods, and performs significantly better. It is also empirically established that CRecSys is able to effectively deal with some of the open challenges like cold-start and limited content problems of the traditional recommender systems.

CRecSys: A Context-Based Recommender System Using Collaborative Filtering and LOD