IJTEEE

Open Access Journal of Scientific, Technology & Engineering Research


International Journal of Technology Enhancements and Emerging Engineering Research (ISSN 2347-4289)
QUICK LINKS
CURRENT PUBLICATIONS



IJTEEE >> Volume 3 - Issue 3, March 2015 Edition



International Journal of Technology Enhancements and Emerging Engineering Research  
International Journal of Technology Enhancements and Emerging Engineering Research

Website: http://www.ijteee.org

ISSN 2347-4289



Exploring Approaches Of Recommendation System In Support Of Verdict And Comparison: A Per-sonalized Prospect

[Full Text]

 

AUTHOR(S)

K.V. Bhosle, Rohit. A. Kautkar

 

KEYWORDS

Keywords : Recommendation System, Collaborative Filtering, Content Based Filtering, Hybrid Filtering, Personalization, Information Overloading

 

ABSTRACT

ABSTRACT: In recent years, the WWW has seen rapid expansion in several web fields. It lead to large volume of Information on web and because of which the Information Overloading delimma arisen in various domains of web. Besides that, just bulky assortment of data lacking knowledge, its of no use. So Recommendation Systems came for rescue with Personalization which permit to present contents to user founded on explored patterns using data mining techniques. At begin of Work, the surroundings of Recommendation Systems and Personalization is presented. With this, subsequently the comprehensive outline of Recommendation System approaches like Collaborative Filtering, Content Based Filtering and Hybrid Filtering with other approaches described. So while prefering specific, one must be capable to differtiate among them using diverse parameters like advantages, disadvantages, underlying principle etc. so this work is for presenting the comparison and verdict of various approaches.

 

REFERENCES

[1] Pazzani, M. J. (1999). A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 13(5-6), 393-408.

[2] Cho, Y. H., Kim, J. K., & Kim, S. H. (2002). A personalized recommender system based on web usage mining and decision tree induction. Expert Systems with Applications, 23(3), 329-342.

[3] Frasincar, F., IJntema, W., Goossen, F., & Hogenboom, F. (2011). A semantic approach for news recommendation. ME Zorrilla and J.-N. Mazón and Ó. Ferrández and I. Garrigós and F. Daniel and J. Trujillo (ed.) Business Intelligence Applications and the Web: Models, Systems and Technologies. IGI Global.

[4] Liu, J., Dolan, P., & Pedersen, E. R. (2010, February). Personalized news recommendation based on click behavior. In Proceedings of the 15th international conference on Intelligent user interfaces (pp. 31-40). ACM.

[5] Burke, R. (2007). Hybrid web recommender systems. In The adaptive web (pp. 377-408). Springer Berlin Heidel-berg.

[6] Breese, J. S., Heckerman, D., & Kadie, C. (1998, July). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence (pp. 43-52). Morgan Kaufmann Publishers Inc..

[7] Pazzani, M. J. (1999). A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 13(5-6), 393-408.

[8] Lemire, D., & McGrath, S. (2005). Implementing a rating-based item-to-item recommender system in php/sql. D-01, On delette. com.

[9] Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender systems handbook (pp. 1-35). Springer US.

[10] Pazzani, M. J., & Billsus, D. (2007). Content-based rec-ommendation systems. In The adaptive web (pp. 325-341). Springer Berlin Heidelberg.

[11] Chen, T., Han, W. L., Wang, H. D., Zhou, Y. X., Xu, B., & Zang, B. Y. (2007, August). Content recommendation system based on private dynamic user profile. In Machine Learning and Cybernetics, 2007 International Conference on (Vol. 4, pp. 2112-2118). IEEE.

[12] Kohrs, A., & Merialdo, B. (1999). Clustering for collaborative filtering applications. In In Computational Intelligence for Modelling, Control & Automation. IOS.

[13] Kumar, P. V., & Reddy, V. R. A Survey on Recommender Systems (RSS) and Its Applications.

[14] Kautkar Rohit, A. A COMPREHENSIVE SURVEY ON DATA MINING.

[15] Leavitt, N. (2006). Recommendation technology: Will it boost e-commerce?. Computer, 39(5), 13-16.

[16] Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender systems handbook (pp. 1-35). Springer US.

[17] Bian, J., Dong, A., He, X., Reddy, S., & Chang, Y. (2013). User Action Interpretation for Online Content Optimization. Knowledge and Data Engineering, IEEE Transactions on, 25(9), 2161-2174.

[18] Cho, Y. H., Kim, J. K., & Kim, S. H. (2002). A personalized recommender system based on web usage mining and decision tree induction. Expert Systems with Applications, 23(3), 329-342.

[19] Wang, T., Yang, A., & Ren, Y. (2009, January). Study on personalized recommendation based on collaborative filtering. In L. Xi (Ed.), WSEAS International Conference. Proceedings. Mathematics and Computers in Science and Engineering (No. 3). World Scientific and Engineering Academy and Society.

[20] Asanov, D. (2011). Algorithms and methods in recom-mender systems. Berlin Institute of Technology, Berlin, Germany.

[21] Kohrs, A., & Merialdo, B. (1999). Clustering for collaborative filtering applications. In In Computational Intelligence for Modelling, Control & Automation. IOS.

[22] Wei, Z., Xun, J., & Wang, X. (2009). One-class classification based finance news story recommendation. Journal of Computational Information Systems5, 6, 1625-1631.

[23] Ekstrand, M. D., Riedl, J. T., & Konstan, J. A. (2011). Collaborative filtering recommender systems. Foundations and Trends in Human-Computer Interaction, 4(2), 81-173.

[24] Basilico, J., & Hofmann, T. (2004, July). Unifying collaborative and content-based filtering. In Proceedings of the twenty-first international conference on Machine learning (p. 9). ACM.

[25] Lee, W. S. (2001, June). Collaborative learning for re-commender systems. In ICML (Vol. 1, pp. 314-321).

[26] Das, A. S., Datar, M., Garg, A., & Rajaram, S. (2007, May). Google news personalization: scalable online collaborative filtering. In Proceedings of the 16th international conference on World Wide Web (pp. 271-280). ACM.

[27] Said, A., Jain, B. J., & Albayrak, S. (2012, March). Analyzing weighting schemes in collaborative filtering: cold start, post cold start and power users. In Proceedings of the 27th Annual ACM Symposium on Applied Computing (pp. 2035-2040). ACM.

[28] Joshi, A., Patankar, A., Chabada, G. K., & Sawant, A. Combining Personalized and Non-Personalized Recom-mendations.

[29] Linden, G., Smith, B., & York, J. (2003). Amazon. com recommendations: Item-to-item collaborative filtering. Internet Computing, IEEE, 7(1), 76-80.

[30] Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in artificial intelligence, 2009, 4.

[31] Anil, K. R. Content Optimization for Personalized News Recommendation: An Experimental CTR Based Ap-proach.