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Combining collaborative filtering and sequential pattern mining for recommendation in e-learning environment

  • Yi Li*
  • , Zhendong Niu
  • , Wei Chen
  • , Wenshi Zhang
  • *Corresponding author for this work
  • Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we describe a Web log mining approach to recommend learning items for each active learner based on the learner's historical learning track. The proposed method is composed of three parts: discovering content-related item sets by Collaborative Filtering (CF), applying the item sets to Sequential Pattern Mining (SPM) and generating sequential pattern recommendations to learners. Different from other recommendation strategies which use CF or SPM separately, this paper combines the two algorithms together and makes some optimizations to adapt them for E-learning environments. Experiments are conducted for the evaluation of the proposed approach and the results show good performance of it.

Original languageEnglish
Title of host publicationAdvances in Web-Based Learning, ICWL 2011 - 10th International Conference, Proceedings
Pages305-313
Number of pages9
DOIs
Publication statusPublished - 2011
Event10th International Conference on Advances in Web-Based Learning, ICWL 2011 - Hong Kong, China
Duration: 8 Dec 201110 Dec 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7048 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Advances in Web-Based Learning, ICWL 2011
Country/TerritoryChina
CityHong Kong
Period8/12/1110/12/11

Keywords

  • E-learning
  • collaborative filtering
  • recommendation
  • sequential pattern

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