An Optimal Enhancement of the Dynamic Features of Recommender Systems
R. Lavanya1, Rithika Lahari2, Palak Gupta3

1R. Lavanya, Department of CSE, SRM IST, Potheri (Tamil Nadu), India.
2Rithika Lahari, Department of CSE, SRM IST, Potheri (Tamil Nadu), India.
3Palak Gupta, Department of CSE, SRM IST, Potheri (Tamil Nadu), India.
Manuscript received on 02 July 2019 | Revised Manuscript received on 12 August 2019 | Manuscript Published on 27 August 2019 | PP: 51-55 | Volume-8 Issue-2S4 July 2019 | Retrieval Number: B10090782S419/2019©BEIESP | DOI: 10.35940/ijrte.B1009.0782S419
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Abstract: Recommendation systems come under the domain of Data mining and Big Data analytics. It is useful tool that is used to predict the ratings or preferences of a user from a pool of resources. The preferences of user are dynamic in nature. The immeasurable usage of internet is having a great impact on the way we deal our lives and communicate with each other. As a result, the requirements of user browsing the internet are changing radically. Recommender Systems (RSs) provide a technology that helps users in finding relevant or preferential information among the pool of information using internet. This paper puts forward not only the issues related to the dynamic nature of user’ requirements but also the changes in the systems’ contents. The Recommendation Systems which involves the above stated issues are termed as Dynamic Recommender Systems (DRSs). This paper first defines the concept of DRS and then explores the various parameters that is taken into account in developing a DRS. This paper also discusses the scope of contributions in this field and concludes citing in possible extensions that can improve the dynamic qualities of recommendation systems in future.
Keywords: Big Data Analytics, Dynamic Users, Collaborative Filtering, Recommendation System.
Scope of the Article: Big Data Security