Research on A Distributed Collaborative Filtering Algorithm
Collaborative filtering (CF) is one of the widely used methods for recommendation system, which uses k-nearest neighbor searching algorithm (KNNS) to find the objects neighbors and recommends an item to the object based on the users rating table or the objects preferences. However, the current KNNS uses Euclidean distance to index dataset and retrieve the search object, which is not suitable for CF. And existing centralized KNNS does not scale up to large volume of data because the response time is linearly increasing with the size of the searched file. In this paper, a distributed KNNS based on angular similarity (DAS-KNNS) and a distributed CF (DCF) based on DAS-KNNS are proposed. DAS-KNNS firstly proposes the distributed indexing structure (DAS-INDEX) based on angular similarity, which refers to the axis and a reference-line to organize the dataset into some shell-hyper-cone, and linearly stores them at each peer. Then it determines the object peer where the search object locates, makes a search hyper-cone which takes the line connecting the origin point and the search object as the axis, and determines those peers which intersect the hyper-cone. Thirdly those peers parallelly search the k-nearest neighbors of the search object. Finally DCF based DAS-KNNS is proposed, whose performance is superior to those other CF.
recommendation system distributed k-nearest searching collaborative filtering
Xiaopeng Yu Xiaogao Yu
School of Economic Management, Wuhan Institute of Technology, Wuhan, 400073, China HuBei University of Economics, Wuhan, 430205, China
国际会议
第八届武汉电子商务国际会议(The Eighth Wuhan International Conference on E-Business)
武汉
英文
937-942
2009-05-30（万方平台首次上网日期，不代表论文的发表时间）