RSR: Related Search Recommendation with Us er Feedback Session

Sejal, D. and Vinuth, C. and Vijay, M. and Venugopal, K.R. and Sundaraja, S.I. (2016) RSR: Related Search Recommendation with Us er Feedback Session. the Proceedings of the 9th European Conference on Data Mining 2015, 11 (1). pp. 81-98. ISSN 1646-3692

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Keyword based search is extensively used method to discover knowledge on the web. Generally, web users unable to arrange and define input queries relevant to their search because of adequate knowledge about domain. Hence, the input queries are normally short and ambiguous. Query recommendation is a method to recommend web queries that are related to the user initial query which helps them to locate their required information more precisely. It also helps the search engine to return appropriate answers and meet their needs. Usually users have ambiguous keywords in their mind to represent their information need. Hence, it is not a good idea to generate relation between user query keywords for recommendations. In this paper, we have presented Related Search Recommendation (RSR) framework, which discovers keywords which are present in snippets clicked and unclicked documents in feedback session. Pseudo documents are generated from feedback sessions which reflect what users wish to retrieve. Finally, semantic similarity is calculated between the terms present in pseudo document and used for recommendations. The proposed method provides semantically related search queries for the given input query. Simulation results show that the proposed framework RSR outperforms Rocchio's model and Snippet Click Model.

Item Type: Article
Subjects: Faculty of Engineering > Computer Science & Information Science Engineering
Divisions: University Visvesvarayya College of Engineering > Department of Computer Science and Information Science Engineering
Depositing User: Ms. Shwetha A C
Date Deposited: 12 Oct 2021 07:26
Last Modified: 12 Oct 2021 07:26

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