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

Linked Open Data (LOD) is an emerging Web technology to store and publish structured data in the form of interlinked knowledgebases like <monospace>DBpedia</monospace>, 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, <monospace>CRecSys</monospace>, for item ratings prediction in movie domain. <monospace>CRecSys</monospace> extracts item-based contextual features from the underlying dataset and generates an <monospace>RDF</monospace> 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. <monospace>CRecSys</monospace> is experimentally evaluated over two movie datasets, one is generated by the authors and second is the <monospace>MovieLens-1M</monospace> benchmark dataset. <monospace>CRecSys</monospace> is also compared with ten baselines and two state-of-the-art recommendation methods, and performs significantly better. It is also empirically established that <monospace>CRecSys</monospace> is able to effectively deal with some of the open challenges like <italic>cold-start</italic> and <italic>limited content</italic> problems of the traditional recommender systems.

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