A Collaborative Web Recommendation System Based on Fuzzy Association Rule Mining Techniques
Currently the use of web recommendation techniques is growing worldwide with aim of providing the custom-made required data to end users. Most of the web users complain about finding useful information on web sites. Web recommender systems predict the information needs of users and provide them with recommendations to facilitate their navigation. Recommender systems have been extensively explored in web mining. The web recommendation systems are split into two main categories such as collaborative recommendation system and content based recommendation system. However, the quality of recommendations and the user satisfaction with such systems are still not optimal. In adaptive association recommender systems based on web mining techniques have strengths and weaknesses. One of these weaknesses is lack of consideration of an appropriate measure to calculate the user’s interest degree of pages. Applying Fuzzy Association Rule Mining (FARM) algorithm to a recommender system leads to calculate user’s interest of pages more accurately. Consequently generated recommendations will be more desirable when compared to other association rule mining techniques.
Copyright © 2014 Praise Worthy Prize - All rights reserved.
Burke, Robin (2007), "Hybrid web recommender systems", In The adaptive web, pp. 377-408.
Jin, Xin, Yanzan Zhou, and Bamshad Mobasher (2005), "A maximum entropy web recommendation system: combining collaborative and content features", In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pp. 612-617.
Jalali, Mehrdad, Norwati Mustapha, Md Nasir Sulaiman, and Ali Mamat (2010), "WebPUM: A Web-based recommendation system to predict user future movements", Expert Systems with Applications 37, no. 9: 6201-6212.
Lops, Pasquale, Marco De Gemmis, and Giovanni Semeraro (2011), "Content-based recommender systems: State of the art and trends." In Recommender systems handbook, pp. 73-105.
Zheng, Zibin, Hao Ma, Michael R. Lyu, and Irwin King (2011), "Qos-aware web service recommendation by collaborative filtering", Services Computing, IEEE Transactions on 4, no. 2: 140-152.
Ekstrand, Michael D., John T. Riedl, and Joseph A. Konstan (2011), "Collaborative filtering recommender systems", Foundations and Trends in Human-Computer Interaction 4, no. 2: 81-173.
Zheng, Vincent W., Yu Zheng, Xing Xie, and Qiang Yang(2010), "Collaborative location and activity recommendations with gps history data", In Proceedings of the 19th international conference on World wide web, pp. 1029-1038.
Jiang, Yechun, Jianxun Liu, Mingdong Tang, and Xiaoqing Liu (2011), "An effective web service recommendation method based on personalized collaborative filtering", In Web Services (ICWS), 2011 IEEE International Conference on, pp. 211-218.
Ho, George TS, W. H. Ip, C. H. Wu, and Y. K. Tse (2012), "Using a fuzzy association rule mining approach to identify the financial data association", Expert Systems with Applications 39, no. 10: 9054-9063.
Roy, Aritra, and Rajdeep Chatterjee (2014), "ntroducing New Hybrid Rough Fuzzy Association Rule Mining Algorithm", In Int. Conf. on Recent Trends in Information, Telecommunication and Computing, ITC.
Lin, Chun‐Wei, and Tzung‐Pei Hong (2013), "A survey of fuzzy web mining", Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3, no. 3: 190-199.
Wanaskar, Ujwala, Sheetal Vij, and Debajyoti Mukhopadhyay (2013), "A Hybrid Web Recommendation System Based on the Improved Association Rule Mining Algorithm", arXiv preprint arXiv:1311.7204.
Tyagi, Shweta, and Kamal K. Bharadwaj (2013), "Enhancing collaborative filtering recommendations by utilizing multi-objective particle swarm optimization embedded association rule mining", Swarm and Evolutionary Computation 13: 1-12.
Thomas, Binu, and G. Raju (2013), "A Novel Web Classification Algorithm Using Fuzzy Weighted Association Rules", International Scholarly Research Notices2013.
García, Enrique, Cristóbal Romero, Sebastián Ventura, and Carlos de Castro (2011), "A collaborative educational association rule mining tool", The Internet and Higher Education 14, no. 2: 77-88.
Ambika, M., Latha, K., Web mining: The demystification of multifarious aspects, (2014) International Review on Computers and Software (IRECOS), 9 (1), pp. 135-141.
Zakrani, A., Idri, A., Applying radial basis function neural networks based on fuzzy clustering to estimate web applications effort, (2010) International Review on Computers and Software (IRECOS), 5 (5), pp. 516-524.
John, J.M., Shajin Nargunam, A., Similarity distance based clustering framework for aggregation of web usage data, (2013) International Review on Computers and Software (IRECOS), 8 (1), pp. 287-295.
- There are currently no refbacks.
Please send any question about this web site to firstname.lastname@example.org
Copyright © 2005-2020 Praise Worthy Prize