Improving Stability, Smoothing and Diversifying of Recommender Systems
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
www.ijsr.net | Open Access | Fully Refereed | Peer Reviewed International Journal

ISSN: 2319-7064



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Research Paper | Information Technology | India | Volume 5 Issue 7, July 2016

Improving Stability, Smoothing and Diversifying of Recommender Systems

Sagar Sontakke, Pratibha Chavan

Recommender systems are used extensively now-a-days for various web-sites such as for providing products suggestions based on customers purchase history and searched product s. Current recommendation system approaches lack of a high degree of stability. Diversification of prediction is also important feature of recommender system. Having displayed same set of results every time may increase lack of trust in recommendation systems. In this paper, our focus is on stability of different recommender system approaches and providing diversity in results. We will focus on different recommender system approaches, methods provided to improve the stability and approaches in increasing diversity of recommender system.

Keywords: Recommendation Systems, Stability, Iterative Smoothing, Collaborative Filtering, Jaccard Distance, Clustering

Edition: Volume 5 Issue 7, July 2016

Pages: 1920 - 1924

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How to Cite this Article?

Sagar Sontakke, Pratibha Chavan, "Improving Stability, Smoothing and Diversifying of Recommender Systems", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=ART2016674, Volume 5 Issue 7, July 2016, 1920 - 1924

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