Characterizing browsing strategies in the World-Wide Web
Proceedings of the Third International World-Wide Web conference on Technology, tools and applications
Using terminological feedback for web search refinement: a log-based study
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Mining the search trails of surfing crowds: identifying relevant websites from user activity
Proceedings of the 17th international conference on World Wide Web
The query-flow graph: model and applications
Proceedings of the 17th ACM conference on Information and knowledge management
Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs
Proceedings of the 17th ACM conference on Information and knowledge management
Dr. Searcher and Mr. Browser: a unified hyperlink-click graph
Proceedings of the 17th ACM conference on Information and knowledge management
Beyond DCG: user behavior as a predictor of a successful search
Proceedings of the third ACM international conference on Web search and data mining
Studying trailfinding algorithms for enhanced web search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Assessing the scenic route: measuring the value of search trails in web logs
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Intent boundary detection in search query logs
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Why searchers switch: understanding and predicting engine switching rationales
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Find it if you can: a game for modeling different types of web search success using interaction data
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
| Hi-index | 0.00 |
Users search and browse activity mined with special toolbars is known to provide diverse valuable information for the search engine. In particular, it helps to understand information need of a searcher, her personal preferences, context of the topic she is currently interested in. Most of the previous studies on the topic either considered the whole user activity for a fixed period of time or divided it relying on some predefined inactivity time-out. It helps to identify groups of web sites visited with the same information need. This paper addresses the problem of automatic segmentation of users browsing logs into logical segments. We propose a method for automatic division of their daily activity into intent-related parts. This segmentation advances the commonly used approaches. We propose several methods for browsing log partitioning and provide detailed study of their performance. We evaluate all algorithms and analyse contributions of various types of features.