Similarity Measure Based on Hierarchical Pair-Wise Sequence
Quan Sen Sun, Nengqiang He, Lei Xu, Yipeng Li, Yong Ren · 2012
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.