Please use this identifier to cite or link to this item:
Title: DQR : a probabilistic approach to diversified query recommendation
Authors: Li, RUI
Kao, B
Bi, B
Cheng, R
Lo, E 
Issue Date: 2012
Source: CIKM '12 Proceedings of the 21st ACM International Conference on Information and Knowledge Management, San Francisco, California, USA, October 27 - November 01, 2013, p. 16-25
Abstract: Web search queries issued by casual users are often short and with limited expressiveness. Query recommendation is a popular technique employed by search engines to help users refine their queries. Traditional similarity-based methods, however, often result in redundant and monotonic recommendations. We identify five basic requirements of a query recommendation system. In particular, we focus on the requirements of redundancy-free and diversified recommendations. We propose the DQR framework, which mines a search log to achieve two goals: (1) It clusters search log queries to extract query concepts, based on which recommended queries are selected. (2) It employs a probabilistic model and a greedy heuristic algorithm to achieve recommendation diversification. Through a comprehensive user study we compare DQR against five other recommendation methods. Our experiment shows that DQR outperforms the other methods in terms of relevancy, diversity, and ranking performance of the recommendations.
Keywords: Diversification
Query concept
Query recommendation
ISBN: 978-1-4503-1156-4
DOI: 10.1145/2396761.2396768
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record


Last Week
Last month
Citations as of Aug 21, 2020

Page view(s)

Last Week
Last month
Citations as of Sep 21, 2020

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.