Exploration of Collaboration Filtering Techniques for Product Recommendation
Ajith Kumar1, P. Madhavan2
1Ajith Kumar, Master of Technology, Department of Computer Science and Engineering, SRM Institute of Science and Technology, KTR Campus, Chennai, Tamilnadu, India.
2Dr. P. Madhavan, Associate Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology, KTR Campus, Chennai, Tamilnadu, India.
Manuscript received on February 01, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 799-802 | Volume-9 Issue-3, February, 2020. | Retrieval Number: C5348029320/2020©BEIESP | DOI: 10.35940/ijeat.C5348.029320
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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: Today, recommendation system has been globally adopted as the most effective and reliable search engine for knowledge extraction in the field of education, economics and scientific research. Collaborative filtering is a proven techniques used in recommender system to make predictions or recommendations of the unknown preferences for users based on the known user preferences. In this paper, collaborative filtering task and their challenges are explored, study the different recommendation techniques and evaluate their performance using different metrics.
Keywords: Collaborative Filtering, Knowledge Extraction, Recommender System.