Abstract

With the increase in the amount of information available on the internet, there is a challenge of providing relevant and useful information to the interesting users on the basis of their interest although when user wants to search data of their interest, they have to search in whole databases, which is very tedious and time consuming too. So a system is needed which provide useful information based on user interest named Recommendation System. A Recommendation System is a sturdy and valuable tool used for decision making and provides a ranking of the most popular items based on user preference. Various algorithms were proposed by different researchers for recommendation of web pages, items, movie, video etc. This paper gives us a snapshot of latest work accomplished in the field of recommendation.

References

• Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl,"Item Based Collaborating Filtering Recommendation Algorithms", ACM, 2001 Hong Kong.
• Shiramshetty Gouthami, Golamari. Jose Mary and Pulluri Srinivas Rao,"Ranking Popular Items by Naive Bayes Algorithm", International Journal of computer science and information technology (IJCSI) Vol 4. No 1, Feb 2012, pp 147-163.
• Gediminas Adomavicius, YoungOk Kwon," Improving Aggregate Recommendation. Diversity Using Ranking-Based Techniques", IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 24, NO. 5, MAY 2012.
• Xiang Cui,Guisheng Yin," Method of collaborative filtering based on uncertain user interests cluster",JOURNAL OF COMPUTERS ,VOL. 8,NO. 1,JANUARY 2013,PP186-193.
• Jian Chen ,Jin Huang, Huaqing Min, "Easy Recommendation Based on Probability Model", Proceeding in Fourth International Conferences on Semantics, Knowledge and Grid, IEEE,2008,pp 441-444.
• Toine Bogers, "Movie Recommendation uses Random Walk Over Contextual Graph", 2010.
• Yi Cai, Ho-fung Leung, Qing Li, Huaqing Min,Jie tang and Juanzi Li, "Typicality-based Collaborative Filtering Recommendation",IEEE TRANSACTION ON KNOWLEDGE AND DATA ENGINEERING,Jan 2013.
• Ralf Krestel, peter Fankhauser, Wolfgang Nejdl, "Latent Dirichlet Allocation for tag Recommendation",ACM 2009.
• Jun Hu , Bing Wang, Yu Liu ,De-Yi Li," Personalized Tag Recommendation Using Social Influence ",JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 27(3): 527-540 May 2012.
• Anastation Noulas, Salvatore Seellaoto,Neal Lathia,Cecilia Mascolo,"Random Walk Around the City :New Venue Recommendation in Location-Based Social Networks", 2012.
• Shumeet Baluja, Rohan Seth,D. Sivakumar, Yushi Jing, Jay Yagnik, Shankar Kumar, Deepak Ravichandran, Mohamed Aly,"Video Suggestion and Discovery for YouTube: Taking Random walks Through the View Graph", ACM , Beijing, China, April 2008.
• Wenpu Xing and Ali Ghorbani, "Weighted PageRank Algorithm", Proceedings of the Second Annual Conference on Communication Networks and Services Research (CNSR'04), 2004 IEEE
• Debajyoti Mukhopadhyay, Pradipta Biswas,"FlexiRank: An algorithm offering flexibility and accuracy for ranking the web pages". Springer-Verlag Berlin Heidelberg 2005, pp-308-313.
• Catherine Benincasa, Adena Calden, Emily Hanlon, Matthew Kindzerske, Kody Law, Eddery Lam, John Rhoades, Ishani Roy, Michael Satz, Eric Valentine and Nathaniel Whitaker, "Page Rank Algorithm", 2006.
• K. Avrachenkov, N. Litvak, D. Nemirovsky, N. Osipova, "Monte Carlo method in PageRank computation: when one iteration is sufficient". SIAM Journal on Numerical Analysis , Volume 45, Issue 2, 2007.
• V. Chitraa, Antony Selvadoss Thanamani, "Recommendation of Web Pages for Online users using Web Log Data", International journal of science and research (IJSR),Jan 2013,pp 345-349.
• MehrdadJalali, Norwati Mustapha, Ali Mamat, Md. Nasir B Sulaiman , 2009, A Recommender System for Online Personalization in the WUM Applications, Proceedings of the World Congress on Engineering and Computer Science 2009 Vol II, San Francisco, USA pp741- 746.
• C. Ding, X. He, P. Husbands, H. Zha, and H. Simon, "Link Analysis: Hubs and Authorities on the World". Technical report: 47847, 2001.