Effective personalization based on association rule discovery from web usage data
Proceedings of the 3rd international workshop on Web information and data management
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Expert Systems with Applications: An International Journal
EWgen: automatic generation of item weights for weighted association rule mining
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Valency based weighted association rule mining
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Weighted association rule mining using particle swarm optimization
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
WeightTransmitter: weighted association rule mining using landmark weights
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Weighted association rule mining via a graph based connectivity model
Information Sciences: an International Journal
Automatic Item Weight Generation for Pattern Mining and its Application
International Journal of Data Warehousing and Mining
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Web recommendation systems based on web usage mining try to mine users' behavior patterns from web access logs, and recommend pages to the online user by matching the user's browsing behavior with the mined historical behavior patterns. Recommendation approaches proposed in previous works, however, do not distinguish the importance of different pageviews, and all the visited pages are treated equally whatever their usefulness to the user. We propose to use pageview duration to judge its usefulness to a user, and try to give more consideration to more useful pageviews, in order to better capture the user's information need and recommend pages more useful to the user. In this paper we try to incorporate pageview weight into the Association Rule (AR) based model and develop a Weighted Association Rule (WAR) model. Comparative experiment of the two shows a significant improvement in the recommendation effectiveness with the proposed WAR model.