Title :
Personalized Recommendation System Based on Web Log Mining and Weighted Bipartite Graph
Author :
Yu Ting ; Cao Yan ; Mu Xiang-wei
Author_Institution :
Transp. Manage. Coll., Dalian Maritime Univ., Dalian, China
Abstract :
Recently, recommendation systems based on bipartite graph algorithm have been widely applied to many areas including E-business, but the weight of edge is ignored. Therefore, the commodity with high rating has not got the priority to be recommended. In order to solve the problem, we propose a personalized recommendation system based on user´s interest. The results of web log mining are introduced to weighted bipartite graph, greatly improving the practicability of the recommendation.
Keywords :
Internet; data mining; electronic commerce; graph theory; recommender systems; Web log mining; e-business; edge weight; high rating commodity; personalized recommendation system; user interest; weighted bipartite graph algorithm; Bipartite graph; Collaboration; Educational institutions; Filtering; Inference algorithms; Resource management; Time complexity; recommendation system; web log mining; weighted bipartite graph;
Conference_Titel :
Computational and Information Sciences (ICCIS), 2013 Fifth International Conference on
Conference_Location :
Shiyang
DOI :
10.1109/ICCIS.2013.161
