Collaborative filtering on streaming data with interest-drifting

Li, Xue, Barajas, Jorge M. and Ding, Yi (2007) Collaborative filtering on streaming data with interest-drifting. Intelligent Data Analysis, 11 1: 75-87.

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Name Description MIMEType Size Downloads
Author Li, Xue
Barajas, Jorge M.
Ding, Yi
Title Collaborative filtering on streaming data with interest-drifting
Journal name Intelligent Data Analysis   Check publisher's open access policy
ISSN 1088-467X
Publication date 2007
Sub-type Article (original research)
Volume 11
Issue 1
Start page 75
End page 87
Total pages 13
Editor A. Famili
Place of publication Amsterdam
Publisher IOS Press
Collection year 2008
Language eng
Subject 280102 Information Systems Management
700000 - Information and Communication Services
Abstract Collaborate filtering is one of the most popular recommendation algorithms. Most collaborative filtering algorithms work with static data. This paper introduces a novel approach to providing recommendations using collaborative filtering when user rating is arrived over an incoming data stream. In this case a large number of data records can arrive rapidly making it impossible to save all of them for later analysis. Moreover, user interests may change over time. By dynamically building a decision tree for every item as data arrive, the incoming data stream is used effectively with a trade off between catching up the changes of users interests and accuracy. By adding a simple step using a hierarchy of items taxonomy, it is also possible to further improve the predicted ratings made by each decision tree and generate recommendations in realtime. Empirical studies with the dynamically built decision trees show that our algorithm works effectively and improves the overall prediction accuracy.
Keyword Computer Science, Artificial Intelligence
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

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Created: Mon, 18 Feb 2008, 17:10:43 EST