Recognizing Topic Change in Search Sessions of Digital Libraries based on Thesaurus and Classification System
This work addresses topic change recognition in digital library search sessions to improve user support, but it is incremental as it applies existing segmentation ideas to a specific domain.
The paper tackled the problem of segmenting user search sessions in digital libraries into topic-coherent parts by proposing a method based on thesaurus and classification systems, with expert evaluation rating it as good for segmentation.
Log analysis in Web search showed that user sessions often contain several different topics. This means sessions need to be segmented into parts which handle the same topic in order to give appropriate user support based on the topic, and not on a mixture of topics. Different methods have been proposed to segment a user session to different topics based on timeouts, lexical analysis, query similarity or external knowledge sources. In this paper, we study the problem in a digital library for the social sciences. We present a method based on a thesaurus and a classification system which are typical knowledge organization systems in digital libraries. Five experts evaluated our approach and rated it as good for the segmentation of search sessions into parts that treat the same topic.