International Journal of Information Engineering and Electronic Business(IJIEEB)
ISSN: 2074-9023 (Print), ISSN: 2074-9031 (Online)
Published By: MECS Press
IJIEEB Vol.9, No.3, May. 2017
A Survey on the Generation of Recommender Systems
Full Text (PDF, 602KB), PP.26-35
In the era of Internet, web is a giant source of information. The constantly growing rate of information in the web makes people confused to decide which product is relevant to them. To find relevant product in today's era is very time consuming and tedious task. Everyday a lot of information is uploaded and retrieved from the web. The web is overloaded with information and it is very essential to cop up with this overloaded and overlooked information. Recommender systems are the solution which can help a user to get relevant information from the bulk of information. Recommender systems provide customized or personalized and non personalized recommendations to interested users. Recommender systems are in its evolution stage. Recommender systems have been evolved from first generation to third generation through second generation. First generation or Web 1.0 recommender systems deal with E-commerce, Second generation or web 2.0 recommender systems use social network and social contextual information for accurate and diverse recommendations, and Third generation recommender systems use location based information or internet of things for generating recommendations. In this paper, three generation of recommender systems and are discussed. Similarity measures and evaluation metrics are used in these generations are also discussed.
Cite This Paper
Rahul Singh, Kanika chuchra, Akshama Rani,"A Survey on the Generation of Recommender Systems", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.9, No.3, pp.26-35, 2017. DOI: 10.5815/ijieeb.2017.03.04
G. Adomavicius, and A. Tuzhilin, "Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions," IEEE Transactions on Knowledge and Data Engineering, vol. 17, pp. 734-749, 2005.
J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, "Recommender systems survey," Knowledge-Based Systems, vol. 46, pp. 109-132, 2013.
J. Schafer, J. Konstan, and J. Riedl, "Recommender Systems in E-Commerce," In proceeding of ACM conference on Electronic commerce in Computing Systems, pp. 158-166, 1999.
N. Antonopoulus, and J. Safer "Cinema screen recommender agent: combining collaborative and content based filtering," IEEE Intelligent Systems, vol. 21, pp. 35-41, 2006.
M. J. Pazaani, "A Framework for collaborative, content based and Demographic Filtering," Artificial Intelligent Review, vol. 13, pp. 393-408, 1999.
R. Burke, "Hybrid recommender systems: survey and experiments," User Modeling and User-Adapted Interaction, vol. 12, pp. 331-370, 2002
D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, "Using Collaborative Filtering to weave an information Tapestry," Communications of the ACM, pp.61-70, 1992.
H. Ma, T. CH. Zhou, M. R. Lyu, and I. King, "Improving recommender systems by incorporating social contextual information," ACM Transactions on Information Systems, vol. 29, pp. 1-23, 2011.
S. Tan, J. Bu, CH. Chen, and X. He, "Using rich social media information for music recommendations via hypergraph model," ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 7, pp. 1-22, 2011.
C .Chen, J. Zeng, X. Zheng, D. Chen, "Recommender systems Based on Social Trust Relationships", IEEE 10th International Conference on e-Business Engineering, pp. 32-37, 2013.
W. Carrer-Neto, M. L. Hernandez-Alcaraz, R. Valencia-Garcia, and, F. Garcia-Sanchez, "Social knowledge based recommender systems. Application to the movie domain," Expert Systems with Applications, vol. 39, pp. 10990-11000, 2012.
A. A. Kardan, and M. Hooman, "Targeted Advertisement in Social Networks using Recommender Systems," 7th International Conference on e-Commerce in Developing Countries: With Focus on e-security (ECDC), pp. 1-13, 2013.
Y. Li, C. Wu, and C. Lai, "A social recommender mechanism for e-commerce: combining similarity, trust, and relationship," Decision Support Systems, Vol. 55, pp. 740-752, 2013.
J. Bobadilla, A. Hernando, F. Ortega and A. Gutiérrez, "Collaborative filtering based on significances," Information Sciences vol.185, pp.1–17, 2012.
Y. Koren, R. Bell, and C. Volinsky, "Matrix Factorization Techniques For Recommender Systems," IEEE Computer Society, vol. 42, pp. 30-37, 2009.
J. Herlocker, J. Konstan, A. Brochers, and J. Riedl, "An algorithmic framework for performing collaborative filtering," Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pp. 230-237, 1999.
B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Item-based collaborative filtering recommendation algorithms," Proceedings of the 10th international conference on World Wide Web, pp. 285-295, 2001.
F. Ortega, J. Sánchez, J. Bobadilla and A. Gutiérrez, "Improving collaborative filtering-based recommender systems results using Pareto dominance," Information Sciences, vol.239, pp.50-61, 2013.
J. Bobadilla, F. Ortega and A. Hernando, "A collaborative filtering similarity measure based on singularities," Information Processing and Management, vol.48, pp.204-217, 2012.
S. Walunj and K. Sadafale, "An Online Recommendation System for E-commerce Based on Apache Mahout Framework,'' ACM, 2013.
Z. Xu, Y. Fu, J. Mao and D. Su, "Towards the semantic web: collaborative tag suggestions," In Collaborative web tagging workshop Scotland, 2006.
D. lee, "How to measure the information similarity in unilateral relations: the case study of Delicious," In 10 Proceedings of the International Workshop on Modeling Social Media, ACM, 2010.
B. Sigurbjörnsson and R. V. Zwol, "Flickr tag recommendation based on collective knowledge," In Proceedings of the 17th international conference on worldwide web, pp. 327–336, ACM, 2008.
A. B. Barragáns-Martínez, M. Rey-López, E. Costa-Montenegro and F. A. Mikic-Fonte et al., "Exploiting Social Tagging in a Web 2.0 Recommender System," IEEE Internet Computing, vol. 14, pp. 23-30, 2010.
N. Zheng and Q. Li, "A recommender system based on tag and time information for socialtagging systems," Expert Systems with applications, Vol. 38, pp. 4575-4587, 2011.
K. Tso-sutter, L. Marinho and L. Schmidt-Thieme, "Tag-aware recommender systems by fusion of collaborative filtering algorithms," In Proceedings of the 2008 ACM symposium on Applied computing, pp. 1995-1999, 2008.
S. Zhao, N. Du, A. Nauerz, X. Zhang, Q. Yuan and R. Fu, "Improved recommendation based on collaborative tagging behaviors," In Proceedings of the 13th international conference on Intelligent user interfaces, pp. 413-416, 2008.
H. Kim and H. Kim, "A framework for tag-aware recommender systems," Expert Systems with Applications, vol. 41, pp. 4000-4009, 2014.
O. Arazy, N. Kumar, and B. Shapira, "Improving Social Recommender Systems," IT Professional, vol. 11, pp. 38-44, 2009.
M. Muñoz-Organero, G. A. Ramírez-González, P. J. Muñoz-Merino and C. D. Kloos, "A Collaborative Recommender System Based on Space-Time Similarities," IEEE Pervasive Computing, Vol. 9, pp. 81-87, 2010.
J. Levandoski, M. Sarwat, A. Eldawy, and M. Mokbel, "LARS: A Location-Aware Recommender System," 2012 IEEE 28th International Conference on Data Engineering (ICDE), pp. 450-461, 2012.
M. A. Ghazanfara and A. Prügel-Bennett, "Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems,"Expert Systems with Applications, Vol. 41, pp. 3261-3275, 2014.
Y. Blanco-Fernández, J. J. Pazos-Arias, A. Gil-Solla, M. Ramos-Cabrer and M. López-Nores, "Providing Entertainment by Content-based Filtering and Semantic Reasoning in Intelligent Recommender Systems," IEEE Transactions on Consumer Electronics, Vol. 54, pp. 727-735, 2008.
B. Lika, K. Kolomvatsos and S. Hadjiefthymiades, "Facing the cold start problem in recommender system," Expert Systems with Applications, Vol. 41, pp. 2065-2073, 2014.
C. Chung, P. Hsu and S. Huang, "βP: A novel approach to filter out malicious rating profiles from recommender systems," Decision Support Systems, Vol. 55, pp. 314-325, 2013.
J. Zhan, C. Hsieh, I. Wang, T. Hsu, C. Liau and D. Wang, "Privacy-Preserving Collaborative Recommender Systems," IEEE Transactions on Systems, Man, and Cybernetcis, Part C : Applications and Reviews, Vol. 40, pp. 472-476, 2010.
Z. Hung, H. Chen and D. Zeng, "Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering," ACM Transactions on Information Systems (TOIS), Vol. 22, pp. 116-142, 2004.
F. Gedikli, D. Jannach and M. Ge, "How should I explain? A comparison of different explanation types for recommender systems,"International Journal Human-Computer Studies, Vol. 72, pp. 367-382, 2014.
D. Melamed, B. Shapira and Y. Elovici, "Marcol : A Market-Based Recommender System,"IEEE Intelligent Systems, Vol. 22, pp. 74-78,2007.
A. C. M. Fong, B. Zhou, S. C. Hui, G. Y. Hong and A. Do, "Web Content Recommender System based on Consumer Behavior Modeling," IEEE Transactions on Consumer Electronics, Vol. 57, pp. 962-969. 2011.
S. A. Golder and B. A. Huberman, "Usage patterns of collaborative tagging systems," Journal of Information Science, vol. 32 pp. 198–208, 2006.
A. Ji, C. Yeon, H. Kim and G. Jo, "Collaborative tagging in recommender systems," In Advances in artiﬁcial intelligence (AI2007), pp. 377–386, 2007.
M.Y.H.AI-Shamir, "Power coefficient as a similarity measure for memory-based collaborative recommender systems," Expert Systems with Applications, 2014.
Sharma, R., Vinayak. S., and Singh, R.K., "Guide Me: A Research Work Area Recommender System", Intelligent Systems and Applications 9 30-37 2016.