Total views : 119
A Novel Approach for Book Recommendation using Fuzzy based Aggregation
Objectives: To propose the top books for universities students by using the proposed fuzzy based approach, Ordered Ranked weighted Aggregation method. Methods/Statistical Analysis: The recommendations of books by different universities differ significantly. A staunch aggregation of the differently recommended books by the top ranked universities may lead to vigorous recommendation. We apply Positional Aggregation based Scoring technique, a rank aggregation method for partial list. We have suggested Ordered Ranked Weighted Aggregation (ORWA) operator, which assigns weights to the ranker. Findings: By using proposed technique, the recommendation of top ranked university is preferred over lower ranked universities. The philosophy of ORWA is the fact that the recommendation of a book by a top ranked university will eventually increase the importance of the recommended books. The top 20 books on "Artificial Intelligence" are recommended using PAS and ORWA based techniques. The recommendation would help the users in finding the books of their requirement. Improvements: The relative comparisons between both the discussed techniques PAS and ORWA are discussed and shown graphically. The results indicate a clear improvement of ORWA over PAS.
Fuzzy Techniques, OWA, ORWA, Partial List, Recommendation Technique, Rank Aggregation.
- Kaji N, Kitsuregawa M. Automatic construction of polaritytagged corpus from HTML documents. Proceeding COLING/ACL 2006 Main Conference Poster Sessions; Sydney. 2006 Jul. p. 452–9. CrossRef.
- Popescu AM, Etzioni O. Extracting product features and opinions from reviews. Proceeding of Empirical Methods of Natural Language Processing EMNLP-05; USA. 2005. p. 339–46. CrossRef.
- Zhuang L, Jing F, Yan Zhu X, Zhang L. Movie review mining and summarization. Proceeding 15th ACM International Conference on Information and Knowledge Management CIKM-06; NY. 2006. p. 43–50. CrossRef.
- Jindal N, Liu B. Review spam detection. Proceeding 16th International world Wide Web conference (WWW); Banff, Alberta, Canada. 2007. p. 1189–90. CrossRef.
- Sohail SS, Siddiqui J, Ali R. Ordered ranked weighted aggregation based book recommendation technique: a link mining approach. Proceeding of 14th World Congress on Hybrid Intelligent System; Kuwait. 2014. p. 309–14. CrossRef.
- Beg MMS, Ahmad N. Soft computing techniques for rank aggregation on the World Wide Web. World Wide Web – An International Journal. 2003; 6(1):5–22.
- Ali R. Pro-Mining: Product recommendation using web based opinion mining. International Journal of Computer Engineering and Technology. 2013; 4(6):299–313.
- Hu M, Liu B. Mining and summarizing customer reviews. Proceeding of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD"04; Seattle, Washington, USA. 2004. p. 168–77. CrossRef.
- Hill W, Stead L, Rosenstein M, Furnas G. Recommending and evaluating choices in a virtual community of use. Proceeding of the ACM Conference on Human Factors in Computing Systems; USA. 1995. p. 194–201.
- Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J. Grouplens: An open architecture for collaborative filtering of netnews. Proceeding of the ACM Conference on Computer Supported Cooperative; USA. 1994. p. 175–86. CrossRef.
- Mobasher B, Cooley R, Srivastava J. Automatic personalization based on web usage mining. Communications of the ACM. 2000; 43(8):142–51.
- Cho YH, Kim JK, Kim SH. A personalized recommender system based on web usage mining and decision tree induction. Expert Systems with Applications, Elsevier Science. 2002; 23(2):329–42. CrossRef.
- Kim JK, Cho YH, Kim WJ, Kim JR, Suh JH. A personalized recommendation procedure for Internet shopping support. Electronic Commerce Research and Applications, Elsevier Science. 2002; 1(3-4):301–13. CrossRef.
- Zeng Z. An intelligent e-commerce recommender system based on web mining. International Journal of Business and Management. 2009; 4(7):10–4. CrossRef.
- Sohail SS, Siddiqui J, Ali R. User feedback scoring and evaluation of a product recommendation system, Proceeding of International Conference on Contemporary Computing (IC3); Noida. 2014. p. 525–30.
- Sohail SS, Siddiqui J, Ali R. User feedback based evaluation of a product recommendation system using rank aggregation method. In: El-Alfy EM, Thampi SM, Takagi H, Piramuthu S, Hanne T, editors. Advances in Intelligent Informatics, Advances in Intelligent Systems and Computing Springer; 2014. p. 349–58.
- Liu DR, Shih YY. Integrating AHP and data mining for product recommendation based on customer lifetime value. Information and Management, Elsevier Science. 2005; 42(1):387–400. CrossRef.
- Sarwar B, Karypis G, Konstan J, Riedl J. Analysis of recommendation algorithms for e-commerce. Proceeding of the 2nd ACM Conference on Electronic Commerce; Minneapolis. 2000 Oct. p. 158–67. CrossRef.
- Russell SJ, Peter N. Artificial intelligence: A modern approach. Prentice-Hall; 1995.
- Weng SS, Liu MJ. Feature-based recommendation for onetoone marketing. Expert Systems with Applications. 2004; 26(4):493–508. CrossRef.
- Cheung KW, Kwok JT, Law MH, Tsui KC. Mining customer product ratings for personalized marketing. Decision Support Systems. 2003; 35(2):231–43. CrossRef.
- Goldberg D, Nichols D, Oki B M, Terry D. Using collaborative filtering to weave an information TAPESTRY. Communications of the ACM. 1992; 35(12):61–70. CrossRef.
- Chen RS, Tsai YS, Yeh KC, Yu DH, Sau YB. Using data mining to provide recommendation service, WSEAS Transactions on Information Science and Applications. 2008; 5(4):459– 74.
- Mooney RJ, Roy L. Content-based Book Recommending using learning for text categorization. Proceeding 5th ACM Conference on Digital Libraries; San Antonio, USA. 2000. p. 195–204. CrossRef.
- Jomsri P. Book recommendation system for digital library based on user profiles by using association rule. Proceeding 2014 4th International Conference on Innovative Computing Technology (INTECH); Luton. 2014. p. 130–4. CrossRef.
- Tewari AS, Priyanka K. Book recommendation system based on collaborative filtering and association rule mining for college students. Proceedings of 2014 International Conference on Contemporary Computing and Informatics (IC3I); Mysore. 2014. p. 135–8.
- Mikawa M, Izumi S, Tanaka K. Book recommendation signage system using silhouette-based gait classification. Proceedings of 10th International Conference on Machine Learning and Applications; HI. 2011. p. 416–9. CrossRef.
- Sohail SS, Siddiqui J, Ali R. Book recommendation system using opinion mining technique. Proceedings of International Conference on Advances in Computing, Communications and Informatics (ICACCI); Mysore. 2013. p. 1609–14. CrossRef.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.