Recommendation Systems: Classification, Open Issues and Recent Developments
Deepak Vats1, Avinash Sharma2
1Deepak Vats, Research Scholar, Department of Computer Science & Enginering, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, India. Also with
Assistant Professor, Department of Computer Applications, University Institute of Computing, Chandigarh University, Gharuan, Mohali, Punjab, India.
2Avinash Sharma, Professor, Department of Computer Science & Enginering, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, India.
Manuscript received on 03 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 July 2019 | PP: 3224-3231 | Volume-8 Issue-9, July 2019 | Retrieval Number: J88310881019/19©BEIESP | DOI: 10.35940/ijitee.J8831.078919
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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: When internet is overwhelmed with infinite options then you need intelligent program like recommendation system to help you dug out and prioritize relevant facts. Everyone is confronted with information flood phenomena and the recommendation engine alleviate Information flood on internet. Personalizing information, to each individual is the solution of information flood, is performed by these intelligent systems through searching web. Texts here trace out diverse characteristics concerned with recommendation system and highlight possible recommendation-methodologies capacity in this arena.
Keywords: Challenges, Classification, Collaborative Filtering, Content-Based Filtering, Evaluation Metrics, Recommendation Systems.
Scope of the Article: Classification