文章基本信息

标题：FHCC : A Soft Hierarchical Clustering Approach for Collaborative Filtering Recommendation
本地全文：下载
作者：Kaiman Zeng ; Nansong Wu ; Xiaokun Yang 等
期刊名称：International Journal of Data Mining & Knowledge Management Process
印刷版ISSN：2231-007X
电子版ISSN：2230-9608
出版年度：2016
卷号：6
期号：3
页码：25
DOI：10.5121/ijdkp.2016.6303
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Recommendation becomes a mainstream feature in nowadays e-commerce because of its significantcontributions in promoting revenue and customer satisfaction. Given hundreds of millions of user activitylogs and product items, accurate and efficient recommendation is a challenging computational task. Thispaper introduces a new soft hierarchical clustering algorithm - Fuzzy Hierarchical Co-clustering (FHCC)algorithm, and applies this algorithm to detect user-product joint groups from users’ behavior data forcollaborative filtering recommendation. Via FHCC, complex relations among different data sources can beanalyzed and understood comprehensively. Besides, FHCC is able to adapt to different types ofapplications according to the accessibility of data sources by carefully adjust the weights of different datasources. Experimental evaluations are performed on a benchmark rating dataset to extract user-productco-clusters. The results show that our proposed approach provide more meaningful recommendationresults, and outperforms existing item-based and user-based collaborative filtering recommendations interms of accuracy and ranked position.
关键词：Collaborative Filtering; Recommender system; Hierarchical clustering; Co-clustering