A Survey of Recommendation System: Research Challenges

  ijett-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2013 by IJETT Journal
Volume-4 Issue-5                      
Year of Publication : 2013
Authors : Lalita Sharma , Anju Gera

MLA 

Lalita Sharma , Anju Gera. "A Survey of Recommendation System: Research Challenges ". International Journal of Engineering Trends and Technology (IJETT). V4(5):1989-1992 May 2013. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group.

Abstract

Recommendation is a process which plays an important role in many applications as WWW . The main objective of this paper is to show various challenges regarding to the t echniques that are being used for generating recommendations. Recommendation techniques can be classified in to three major categories: Collaborative Filtering, Content Based and Hybrid Recommendations. By giving the overview of these problems we can impro ve the quality of recommendations by invent ing new approaches and methods, which can be used as a highway for research and practice in this area.

References

[1] Gediminas Adomavicius and Alexander Tuzhilin, “Toward the Next Generation of Recommender Systems: A Survey of the State - of - the - Art and Possible Extens ions”, IEEETKDE: IEEE Transactions on Knowledge and Data Engineering, 17, 2005.
[2] Prodan Andrei - Cristian, “Implementation of a Recommender System Using Collaborative Filtering”, Studia univ. Babes_bolyai, Informatica, volume lv, number 4, 2010.
[3] Michael J. Pazzani and Daniel Billsus, “Content Based Recommendation System.”
[4] Claypool, M., A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. Sartin, “Combining content - based and collaborative filters in an online newspaper”. In ACM SIGIR'99, Workshop on Recomme nder Systems: Algorithms and Evaluation, August 1999.
[5] Debnath, Ganguly and Mitra, “Feature Weighting In Content Based Recommendation System Using Social Network Analysis”.
[6] MovieLens Dataset, USA, 2003, University Minnesota.
[7] Balabanovic, M. and Y. Shoham . Fab, “Content - based, collaborative recommendation”, Communications of the ACM, 40(3):66 - 72, 1997.
[8] Sarwar, Badrul M., George Karypis, Joseph A. Konstan and John T. Riedl, “Application of Dimensionality Reduction in Recommender System – A Case Study”, In ACM WebKDD Workshop, 2000.
[9] K. Goldberg, T. Roeder, D. Gupta, and C. Perkins. Eigentaste, “A constant time collaborative filtering algorithm”, Information Retrieval, 4(2):133 – 151, 2001.
[10] Boddu Raja Sarath Kumarmaddali and Surendra Prasad Babuan:, “Implementation of Content Boosted Collaborative Filtering Algorithm”, IJEST.
[11] J. Wang, A. P. de Vries, and M. J. T. Reinders, “Unifying User - Based and Item - Based Collaborative Filtering Approache s by Similarity Fusion”. In SIGIR ’06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, New York, NY, USA, 2006.
[12] C. Desrosiers and G. Karypis, “Solving the Sparsity Problem: Collaborat ive Filtering via Indirect Similarities”, Technical Report, Dec 2008.
[13] Terveen and Hill, “Beyond Recommendation System in HCI in the New Millennium, Addison - Wesley, 2001 p. 4 of 21.

Keywords
Collaborative Filtering, Content - Based Recommendation, Recommendation System, Sparsity Problem. Cold Start ,over specialization .