TST: Threshold Based Similarity Transitivity Method in Collaborative Filtering with Cloud Computing

查看全文 作　　者：Feng [1]Xie;Zhen [2]Chen;Hongfeng [3]Xu;Xiwei [1]Feng;Qi [4]Hou

机构地区：[1]Department of Automation, Research Institute of Information Technology and Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University;[2]Research Institute of Information Technology and Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University;[3]Department of Computer Science and Technologies and Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University;[4]Department of Electronic Engineering and Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University

出　　处：《Tsinghua Science and Technology》索引2013年第18卷第3期,共10页

基　　金：supported by Ministry of Science and Technology of China under the National Key Basic Research and Development (973) Program of China (Nos. 2012CB315801 and 2011CB302805);the National Natural Science Foundation of China A3 Program (No. 61161140320);the National Natural Science Foundation of China (No. 61233016);supported by Intel Research Council with the title of Security Vulnerability Analysis based on Cloud Platform with Intel IA Architecture 摘　　要：Collaborative filtering solves information overload problem by presenting personalized content to individual users based on their interests, which has been extensively applied in real-world recommender systems. As a class of simple but efficient collaborative filtering method, similarity based approaches make predictions by finding users with similar taste or items that have been similarly chosen. However, as the number of users or items grows rapidly, the traditional approach is suffering from the data sparsity problem. Inaccurate similarities derived from the sparse user-item associations would generate the inaccurate neighborhood for each user or item. Consequently, its poor recommendation drives us to propose a Threshold based Similarity Transitivity (TST) method in this paper. TST firstly filters out those inaccurate similarities by setting an intersection threshold and then replaces them with the transitivity similarity. Besides, the TST method is designed to be scalable with MapReduce framework based on cloud computing platform. We evaluate our algorithm on the public data set MovieLens and a real-world data set from AppChina (an Android application market) with several well-known metrics including precision, recall, coverage, and popularity. The experimental results demonstrate that TST copes well with the tradeoff between quality and quantity of similarity by setting an appropriate threshold. Moreover, we can experimentally find the optimal threshold which will be smaller as the data set becomes sparser. The experimental results also show that TST significantly outperforms the traditional approach even when the data becomes sparser. 关键词：最优阈值过滤方法协同过滤计算平台相似性 TST 个人用户物性

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TST: Threshold Based Similarity Transitivity Method in Collaborative Filtering with Cloud Computing

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