Approaches of Recommender System: A Survey
 |
International Journal of Computer Trends and Technology (IJCTT) | 
|
| © 2016 by IJCTT Journal | ||
| Volume-34 Number-3 |
||
| 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.
Abstract -
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.
References
[1] Harpreet Kaur Virk, Er.Maninder Singh,” Analysis and
Design of Hybrid Online Movie Recommender
System”International Journal of Innovations in Engineering and
Technology (IJIET)Volume 5 Issue 2,April 2015.
[2] Sanjeev Dhawan, Kulvinder Singh, Jyoti,” High Rating
Recent Preferences Based Recommendation System”
4thInternational Conference on Eco-friendly Computing and
Communication Systems,ICECCS 2015
[3]Manoj Kumar,D.K Yadav,Ankur Singh,Vijay Kr. Gupta,” A
Movie Recommender System: MOVREC”
International Journal of Computer Applications (0975 – 8887)
Volume 124 – No.3, August 2015
[4] Manisha Chandak,Sheetal Girase, Debajyoti
Mukhopadhyay,” Introducing Hybrid Technique for
Optimization of Book Recommender System” International
Conference on Advanced Computing Technologies and
Applications(ICACTA-2015)
[5] Utkarsh Gupta1 and Dr Nagamma Patil2,” Recommender
System Based on Hierarchical Clustering Algorithm
Chameleon” 2015 IEEE International Advance Computing
Conference (IACC)
[6] Hirdesh Shivhare, Anshul Gupta, Shalki Sharma,”
Recommender system using fuzzy c-means clustering and
genetic algorithm based weighted similarity measure” IEEE
International Conference on Computer, Communication and
Control (IC4-2015)
[7] Jyoti Gupta, Jayant Gadge,” Performance Analysis of
Recommendation System Based On Collaborative Filtering and
Demographics”2015 International Conference on
Communication, Information & Computing Technology
(ICCICT), Jan. 16-17, Mumbai, India
[8] Suman Datta,Joydeep Das,Prosenjit Gupta and Subhashis
Majumder,” SCARS: A Scalable Context-Aware
Recommendation System”2015 IEEE
[9] Artem Umanets, Artur Ferreira, Nuno Leite,” GuideMe - A
Tourist Guide with a Recommender System
and Social Interaction” Procedia Technology 17 ( 2014 ) 407 –
414
[10] Jayasimha Katukuri, Tolga Könik, Rajyashree Mukherjee,
Santanu Kolay,” Recommending Similar Items in Large-scale
Online Marketplaces” 2014 IEEE International Conference on
Big Data
[11]Zebin Wu,Yan Chen,Taoying Li,”Personalized
Recommendation Based On The Improved Similarity and Fuzzy
Clustering” The National Natural Science Foundation of
China(No.71271034)2014 IEEE
[12] Eric Medvet, Alberto Bartoli, Giulio Piccinin ” Publication
Venue Recommendation based on Paper Abstract” 2014 IEEE
26th International Conference on Tools with Artificial
Intelligence
[13]Ling Yanxiang, Guo Deke, Cai Fei , Chen Honghui,” Userbased
Clustering with Top-N Recommendation on Cold-Start
Problem” 2013 Third International Conference on Intelligent
System Design and Engineering Applications
[14] Hideyuki Mase, Hayato Ohwada,” A Collaborative
Filtering Incorporating Hybrid-Clustering Technology” 2012
International Conference on Systems and Informatics (ICSAI
2012)
[15] Li Chao, Yu Jian, Li Xiang, Chen Jia Hui,” A Social
Network System Oriented Hybrid Recommendation Model”
2012 2nd International Conference on Computer Science and
Network Technology
[16] Chenguang Pan, Wenxin Li,” Research Paper
Recommendation with Topic Analysis” 20IO International
Conference On Computer Design And Appliations (ICCDA
2010)
[17] Kam Fung Yeung, Yanyan Yang,” A Proactive
Personalized Mobile News Recommendation System” 2010
Developments in E-systems Engineering
[18] Jia Rongfei, Jin Maozhong, and Liu Chao,” A New
Clustering Method For Collaborative Filtering” 2010
International Conference on Networking and Information
Technology
[19] Zheng Wan,” Design of a Recommendation Filtering
System in MobileCommerce”2009, IEEE
[20] L. Martínez, R.M. Rodríguez, M. Espinilla,” REJA: A
GEOREFERENCED HYBRID RECOMMENDER SYSTEM
FOR RESTAURANTS” 2009 IEEE/WIC/ACM International
Joint Conferences on Web Intelligence and Intelligent Agent
Technology
Keywords
Recommender System, Collaborative
filtering, Content based filtering, Hybrid
Recommender, Clustering, Similarity.