A Study on Different Phases and Various Recommendation System Techniques
Mallari Vijay Kumar1, P.N.V.S. Pavan Kumar2

1Mallari Vijay Kumar, Assistant Professor, Department of CSE, GPREC, (A.P), India.
2Sri P.N.V.S. Pavan Kumar, Assistant Professor, Department of CSE, GPREC, (A.P), India.
Manuscript received on 05 February 2019 | Revised Manuscript received on 27 March 2019 | Manuscript Published on 28 April 2019 | PP: 38-41 | Volume-7 Issue-5C February 2019 | Retrieval Number: E10100275C19/19©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Now-a-days, recommender systems(RS)are playing a crucial role in human tasks. The online websites like movies, restaurants, education and much more, uses the recommender systems to suggest the customers for E-commerce. Recommender systems make money by attracting users with recommendations. Each system may use different datasets from various sources to analyze the user behavior and to find interesting patterns that predict the user’s future purchase or taste. Most of the times lack of data results to the inappropriate recommendations (means bad recommendations). This paper reviews different phases involved in implementing RS and various recommender methods including the study of thosemethods that are used in several papers of various authors. We also included the advantages and disadvantages of each method. Finally, this paper also gives analyses of various challenges and issues (problems) faced in the implementation of RS algorithms.
Keywords: Phases, Collaborative Filtering, Content-Based Filtering, Hybrid Filtering, Problems.
Scope of the Article: System Integration