Community-Driven Collaborative Recommendation System
Laxmi Chaudhary1, Buddha Singh2
1Laxmi Chaudhary, SC & SS, Jawaharlal Nehru University, New Delhi, India.
2Buddha Singh, SC & SS, Jawaharlal Nehru University, New Delhi, India.
Manuscript received on November 20, 2019. | Revised Manuscript received on November 26, 2019. | Manuscript published on 30 November, 2019. | PP: 3722-3726 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8112118419/2019©BEIESP | DOI: 10.35940/ijrte.D8112.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Collaborative Filtering, Community Detection, Recommendation System, Scalability, Sparsity.
Scope of the Article: Community Information Systems.