Open AccessCommunity-Driven Collaborative Recommendation System
Author(s) -
Laxmi Chaudhary,
Buddha Singh
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d8112.118419
Subject(s) - movielens , collaborative filtering , rss , recommender system , singular value decomposition , computer science , scalability , information retrieval , decomposition , value (mathematics) , data mining , artificial intelligence , machine learning , world wide web , database , ecology , biology
Recommendation systems (RSs) are an application of community detection, becoming more significant in our daily lives. They play a significant role in suggesting information to users such as products, services, friends and so on. A novel community driven collaborative recommendation system (CDCRS) has been proposed by the authors, in this particular paper. Furthermore, K means approach has been utilized to detect communities and extract the relationship among the users. The singular value decomposition method (SVD) is also applied. Issues of sparsity and scalability of the collaborative method are considered. Experiments were conducted on MovieLens datasets. Movie ratings were predicted and top-k recommendations for the user produced. The comparative study that was performed between the proposed as well as the collaborative filtering method dependent on SVD (CFSVD) as well as the results of experiments shows that CFSVD is outperformed by the proposed CDCRS method.