Approaches of Recommender System: A Survey
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2016 by IJCTT Journal|
|Year of Publication : 2016|
|Authors : Prerana Khurana, Shabnam Parveen|
|DOI : 10.14445/22312803/IJCTT-V34P124|
Prerana Khurana, Shabnam Parveen "Approaches of Recommender System: A Survey". International Journal of Computer Trends and Technology (IJCTT) V34(3):134-138, April 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Recommender system is subclass of information filtering system that seek to predict the ‘rating’ or ‘preference’ that a user would give to an item. Recommender system are applied in variety of applications like movies, music, news, academic events, venue, books, research articles, tourism, search queries, social tags and products in general. Recommender systems typically produce a list of recommendations in two ways-through collaborative or content based filtering. Recommender System are useful alternative to search algorithms as they help user to discover items they might not have found by themselves. In this paper we have discussed various approaches of recommender system and techniques applied to implement it. The goal of this work is to discover existing trends, open issues and possible directions for future research.
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Recommender System, Collaborative filtering, Content based filtering, Hybrid Recommender, Clustering, Similarity.