Soft-Computing Based Recommendation System: A Comparative Study
Shreyas Das1, Bhabani Shankar Prasad Mishra2, Manoj Kumar Mishra3, Subhashree Mishra4, Suresh Chandra Moharana5

1Shreyas Das, M.Tech student at KIIT Deemed to be University in the discipline of Computer Science and Engineering.
2Bhabani Shankar Prasad Mishra, Associate Professor in School of Computer Engineering at KIIT University, Bhubaneswar, Odisha since 2006.
3ManojKumarMishra, (BTech) and Postgraduate (MTech) students one and half years of industry experience in Mainframe Technologies.
4Suresh Moharana, Assistant Professor in School of Computer Engineering at KIIT Deemed to be University.
5Subhashree Mishra, Assistant Professor in School of Electronics Engineering at KIIT University, Bhubaneswar since 2013.
Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 131-139 | Volume-8 Issue-8, June 2019 | Retrieval Number: G6099058719/19©BEIESP
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Abstract: In modern days, recommendation system has left a huge impact on the society. It gives personalized list of items or services to its users in a shorter time. When there is a huge number of information, then it needs to be filtered to generate relevant recommendation. There are different filtering techniques used in recommendation system. To optimize its performance some soft computing techniques can also be added along with those filtering techniques. This article gives an overview of evaluation metrics, phases, challenges and how soft computing techniques are merged along with the filtering techniques. This paper also presents some statistical analysis of popularity of various filtering techniques and soft computing techniques used in recommendation system.
Keyword: Challenges and issues, Filtering techniques, Recommendation system, Soft computing.
Scope of the Article: System Integration