Techniques of Recommender System
Harleen kaur1, Gourav Bathla2
1Harleen Kaur, Department of CSE, Chandigarh University, Gahruan, Mohali (Punjab), India.
2Gourav Bathla, Department of CSE, Chandigarh University, Gahruan, Mohali (Punjab), India.
Manuscript received on 20 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 26 August 2019 | PP: 373-379 | Volume-8 Issue-9S August 2019 | Retrieval Number: I10590789S19/19©BEIESP | DOI: 10.35940/ijitee.I1059.0789S19
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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: The term Recommender system is described as any organization that provides personalized suggestions as a result and it effects the user in the individualized way to favorable items from the large number of opinions. The voluminous inflation of the reachable data online and also the number of users have lead to the information overload problem. To overcome this problem the recommender system came into play as it is able to prioritize and personalize the data. Recommendation systems have developed alongside with the net. Recommender system has mainly three data filtering methods such as content based filtering technique, collaborative based filtering technique and the hybrid approach to manage the data overload problem and to recommends the items to the user the items they are interested in from the dynamically generated data. This paper makes a comprehensive introduction to the recommender system with its types, content based filtering , collaborative filtering and the hybrid recommendation.
Keywords: Recommendation System, Collaborative Filtering, Content Based Filtering, Hybrid Recommendation.
Scope of the Article: Software Engineering Techniques and Production Perspectives