Publication Type : Conference Paper
Publisher : 2016 IEEE International Conference on Computational Intelligence and Computing Research,
Source : 2016 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2016, Institute of Electrical and Electronics Engineers Inc., Chennai, India (2017)
Url : http://ieeexplore.ieee.org/document/7919606/
Keywords : Apriori, Artificial intelligence, Book Recommendation, Data mining, E-Commerce applications, Electronic commerce, FPInterscet, opinion mining, Pattern mining algorithms, Recommender systems, Social networking sites, User's preferences, Websites
Campus : Amritapuri
School : Department of Computer Science and Engineering, School of Engineering
Department : Computer Science
Year : 2017
Abstract : Recommendation systems are widely used in ecommerce applications. A recommendation system intends to recommend the items or products to a particular user, based on user's interests, other user's preferences, and their ratings. To provide a better recommendation system, it is necessary to generate associations among products. Since e-commerce and social networking sites generates massive data, traditional data mining approaches perform poorly. Also, the pattern mining algorithm such as the traditional Apriori suffers from high latency in scanning the large database for generating association rules. In this paper we propose a novel pattern mining algorithm called as Frequent Pattern Intersect algorithm (FPIntersect algorithm), which overcomes the drawback of Apriori. The proposed method is validated through simulations, and the results are promising.
Cite this Research Publication : P. Devika, Jisha R. C., and Dr. Sajeev G. P., “A novel approach for book recommendation systems”, in 2016 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2016, Chennai, India, 2017