SynRec: A Prediction Technique using Collaborative Filtering and Synergy Score
Nupur Kalra1, Deepak Yadav2, Gourav Bathla3
1Nupur Kalra, Department of CSE, Chandigarh University, Mohali (Punjab), India.
2Deepak Yadav, Department of CSE, Chandigarh University, Mohali (Punjab), India.
3Gourav Bathla, Department of CSE, Chandigarh University, Mohali (Punjab), India.
Manuscript received on 27 August 2019 | Revised Manuscript received on 03 September 2019 | Manuscript Published on 14 September 2019 | PP: 457-463 | Volume-8 Issue-5S3, July 2019 | Retrieval Number: E10960785S319/19©BEIESP | DOI: 10.35940/ijeat.E1096.0785S319
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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: Recommender systems are techniques designed to produce personalized recommendations. Data sparsity, scalability cold start and quality of prediction are some of the problems faced by a recommender system. Traditional recommender systems consider that all the users are independent and identical, its an assumption which leads to a total ignorance of social interactions and trust among user. Trust relation among users ease the work of recommender systems to produce better quality of recommendations. In this paper, an effective technique is proposed using trust factor extracted with help of ratings given so that quality can be improved and better predictions can be done. A novel-technique has been proposed for recommender system using film-trust dataset and its effectiveness has been justified with the help of experiments.
Keywords: Collaborative Filtering, Recommender System, Social Trust, Synergy Score.
Scope of the Article: Regression and Prediction