Neural network applications for automatic new topic identification

Abstract

Purpose - This study aims to propose an artificial neural network to identify automatically topic changes in a user session by using the statistical characteristics of queries, such as time intervals and query reformulation patterns. Design/methodology/approach - A sample data log from the Norwegian search engine FAST (currently owned by Overture) is selected to train the neural network and then the neural network is used to identify topic changes in the data log. Findings - A total of 98.4 percent of topic shifts and 86.6 percent of topic continuations were estimated correctly. Originality/value - Content analysis of search engine user queries is an important task, since successful exploitation of the content of queries can result in the design of efficient information retrieval algorithms for search engines, which can offer custom-tailored services to the web user. Identification of topic changes within a user search session is a key issue in the content analysis of search engine user queries.