Neural network applications for automatic new topic identification on excite web search engine data logs
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Date
2004
Authors
Spink, Amanda
Journal Title
Journal ISSN
Volume Title
Publisher
Information Today
Abstract
The analysis of contextual information in search engine query logs is an important, yet difficult task. Users submit few queries, and search multiple topics sometimes with closely related context. Identification of topic changes within a search session is an important branch of contextual information analysis. The purpose of this study is to propose a topic identification algorithm using neural networks. A sample from the Excite data log is selected to train the neural network and then the neural network is used to identify topic changes in the data log. As a result, 76% of topic shifts and 92% of topic continuations are identified correctly.
Description
Bu çalışma, 12-17 Kasım 2004 tarihleri arasında Rhode Island[Amerika Birleşik Devletleri]’nde düzenlenen 67. Annual Meeting of the American Society for Information Science and Technology’de bildiri olarak sunulmuştur.
Keywords
Computer science, Information science and library science, Search engine, Topic identification, Session identification, Neural networks, Information-seeking, Context, Users
Citation
Özmutlu, H. C. vd. (2004). “Neural network applications for automatic new topic identification on excite web search engine data logs”. Proceedings of the Asist Annual Meeting, Asist 2004: Proceedings of the 67th Asist Annual Meeting, 41, 310-316.