Improving Recommendations Through Re-Ranking Of Results
| ||International Journal of Computer Trends and Technology (IJCTT)|| |
|© - October Issue 2013 by IJCTT Journal|
|Volume-4 Issue-10 |
|Year of Publication : 2013|
|Authors :S.Ashwini , T.Chandra Sekhara Reddy|
S.Ashwini , T.Chandra Sekhara Reddy"Improving Recommendations Through Re-Ranking Of Results"International Journal of Computer Trends and Technology (IJCTT),V4(10):3496-3500 October Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract:- World Wide Web has become a good source for any kind of information. People of all walks of life depend on it for their information retrieval through queries. However users get huge number of records or recommendations. It causes problem to users as they do not see the intended results immediately. Instead they have to spend some time browsing for intended content. This kind of user experience is not good. For this reason there is a need for improving recommender systems. Ranking is one of the techniques for improving search results. Many ranking techniques came into existence. The recommender systems are using them. In this paper we propose a re-ranking algorithm that improves the ranked results that will give rich user experience. We build a prototype to show the efficiency of the proposed approach. The empirical results revealed that the application is very useful.
 C.J.C. Burges, R. Ragno, and Q.V. Le, “Learning to Rank withNonsmooth Cost Functions,” Proc. Advances in Neural InformationProcessing Systems (NIPS ’06), pp. 193-200, 2006.
 Z. Cao and T. Yan Liu, “Learning to Rank: From PairwiseApproach to Listwise Approach,” Proc. 24th Int’l Conf. MachineLearning (ICML ’07), pp. 129-136, 2007.
 C.J.C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N.Hamilton, and G. Hullender, “Learning to Rank Using GradientDescent,” Proc. 22th Int’l Conf. Machine Learning (ICML ’05), 2005.
 Y. Freund, R. Iyer, R.E. Schapire, Y. Singer, and G. Dietterich, “An Efficient Boosting Algorithm for Combining Preferences,”J. Machine Learning Research, vol. 4, pp. 933-969, 2003.
 R. Herbrich, T. Graepel, and K. Obermayer, “Large Margin RankBoundaries for Ordinal Regression,” Advances in Large MarginClassifiers, pp. 115-132, MIT Press, 2000.
 T. Joachims, “Optimizing Search Engines Using ClickthroughData,” Proc. Eighth ACM SIGKDD Int’l Conf. Knowledge Discoveryand Data Mining (KDD ’02), pp. 133-142, 2002.
 A.Spink, D.Wolfram,B.J.Jansen,T.Saracevis,”Searching the Web :The public and their queries ”.jouurnel of the amercianSocitey for information Science and technology 52(3),2001,226-234.
 R.Cooley, B.Mobasher and J.Srivastava, “Web mining: Information and pattern discovery on the World Wide Web,”. In 9th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 97) 1997.
 M.H.Dunham,Companion slides for the text ,” Data mining Introductory and advanced topics ”.Prentice Hall,2002.
 IsakTaka,SarahZelikovitz,AmandaSpink,”Web Search log to Identify Query Classficationterms”Proceeding of IEEE International Conference on Information Technology (ITNG’07),pp:50-57,2008.
 L. Page,S.Brin ,R.Motwani,T.Winograd,”The pagerankcitration ranking :Bringing order to the Web ”.Technical report ,Stanford Digital Libraries SIDL-WP-1990-0120,1999.
 J.Wen, J.Mie, and H.zhang,” Clustering user queries of a search engine ”.In Proc of 10th International WWW Conference .W3C,2001.
 Thorsten joachims,”optimizing search engine using clickthough data” Proceeding of the 8th ACM SIGKDD international conference on knowledge discovery and data mining ,2002,pp:133-142, New York.
 H.Ma, H.Yang,I.King,and M.R.Lyu,”learning latent semantic relations from clickthough data from query suggestion ”.InCIKM’08:Proceeding oh the 17th ACM conference on information and knowledge management ,pages 709-708,New York,ny,USA,2008,ACM.
 J.L. Herlocker, J.A. Konstan, L.G. Terveen, and J.T. Riedl, “Evaluating Collaborative Filtering Recommender Systems,” ACM Trans. Information Systems, vol. 22, no. 1, pp. 5-53, 2004.
 T. Hofmann, “Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis,” SIGIR ’03: Proc. 26th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 259-266, 2003.
Keywords :— Recommender systems, ranking, re-ranking, recommendations.