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.
35th European Conference on IR Research (ECIR 2013)
4 Apr 2013