Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Visualizing the evolution of Web ecologies
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Using path profiles to predict HTTP requests
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Discovery of Web Robot Sessions Based on their Navigational Patterns
Data Mining and Knowledge Discovery
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Data mining for path traversal patterns in a web environment
ICDCS '96 Proceedings of the 16th International Conference on Distributed Computing Systems (ICDCS '96)
A Web page prediction model based on click-stream tree representation of user behavior
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Selective Markov models for predicting Web page accesses
ACM Transactions on Internet Technology (TOIT)
Off the beaten tracks: exploring three aspects of web navigation
Proceedings of the 15th international conference on World Wide Web
Relevance and Impact of Tabbed Browsing Behavior on Web Usage Mining
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Mining longest repeating subsequences to predict world wide web surfing
USITS'99 Proceedings of the 2nd conference on USENIX Symposium on Internet Technologies and Systems - Volume 2
A low-order markov model integrating long-distance histories for collaborative recommender systems
Proceedings of the 14th international conference on Intelligent user interfaces
Parallel browsing behavior on the web
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Task-Oriented web user modeling for recommendation
UM'05 Proceedings of the 10th international conference on User Modeling
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Present-day web browsers possess several features that facilitate browsing tasks. Among these features, one of the most useful is the possibility of using tabs. Nowadays, it is very common for web users to use several tabs and to switch from one to another while navigating. Taking into account parallel browsing is thus becoming very important in the frame of web usage mining. Although many studies about web users' navigational behavior have been conducted, few of these studies deal with parallel browsing. This paper is dedicated to such a study. Taking into account parallel browsing involves to have some information about when tab switches are performed in user sessions. However, current browsers do not allow to explicitly acquire such an information, and the data available for web usage mining is usually made of raw navigation logs in which parallel sessions are mixed. Therefore, we propose to get this information in an implicit way. We thus propose the TABAKO model, which is able to detect tab switches in raw navigation logs and to benefit from such a knowledge in order to improve the quality of web recommendations. Experimental studies are performed on an open browsing dataset. Results validate the ability of our algorithm to detect parallel sessions, and to exploit them to enhance the results compared to a state-of-the-art recommendation model.