A Recommendation System: Trends and Future
Shefali Gupta1, Meenu Dave2
1Shefali Gupta, Jagannath University, Jaipur (Rajasthan), India.
2Dr. Meenu Dave, Jagannath University, Jaipur (Rajasthan), India.
Manuscript received on 28 September 2019 | Revised Manuscript received on 10 November 2019 | Manuscript Published on 22 November 2019 | PP: 1361-1364 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F12400986S319/19©BEIESP | DOI: 10.35940/ijeat.F1240.0986S319
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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: Recommendation system plays a key role in e-commerce universe and is used in many applications, websites and more. It has led to synergies between applications, created global village and growth of information. This paper represents the overview of approaches and techniques generated in recommendation systems. Recommendation system is categorized in two classes: Personalized and Non-personalized, which is further divided into various approaches and techniques. This paper discusses each of the methodology in detail highlighting their strengths and weaknesses.
Keywords: Recommendation System, Personalized Recommendation System, Non-Personalized Recommendation System, Content Based Filtering, Collaborative Filtering, Knowledge Based Filtering, Hybrid Filtering.
Scope of the Article: Multi-Agent Systems