Filtering of web recommendation lists using positive and negative usage patterns

Authors:
Przemysław Kazienko
Affiliations:
Wrocław University of Technology, Institute of Applied Informatics, Wrocław, Poland
Venue:
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Year:
2007

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Abstract

The typical content-based recommendation systems make use of textual similarity between items. Based on the knowledge about historical user behaviours extracted from the web logs, the content recommendation lists can be verified and filtered: some items are reinforced whereas some other are weakened. Four different usage patterns are used in the filtering process: positive and negative association rules, positive sequential patterns and negative sequential patterns. The last ones are the new pattern concept introduced in the paper.