Similarity Measure Based on Hierarchical Pair-wise Sequence

摘要：

Collaborative filtering systems have achieved great success in both research and business applications. One of the key technologies in collaborative filtering is similarity measure. Cosine-based and Pearson correlation-based methods are popular ways for similarity measure, but have low accuracy. In this paper, we propose a novel method for similarity measure, referred as hierarchical pair-wise sequence (HPWS). In HPWS, we take into account both the sequence property of user behaviors and the hierarchical property of item categories. We design a collaborative filtering recommendation system to evaluate the performance of HPWS based on the empirical data collected from a real P2P application, i.e. byrBT in CERNET. Experiment results show that HPWS outperforms traditional Cosine similarity and Pearson similarity measures under all scenarios.

关键词： Similarity Measure Sequence Matching Hierarchical Graph Collaborative Filtering

作者: Quan Sun Nengqiang He Lei Xu Yipeng Li Yong Ren

作者单位: Department of Electronic Engineering, Tsinghua University, Beijing, China

会议类型: 国际会议

会议名称: 2012 International Conference on Computer Science and Electronic Engineering(2012 IEEE计算机科学与电子工程国际会议 ICCSEE 2012)

会议地点: 杭州

会议语种:英文

页码: 512-516

在线出版日期: 2012-03-23（万方平台首次上网日期，不代表论文的发表时间）