A Probabilistic Approach to Diversified Query Recommendation
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A Probabilistic Approach to Diversified Query Recommendation

A Probabilistic Approach to Diversified Query Recommendation


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About the Book

This dissertation, "A Probabilistic Approach to Diversified Query Recommendation" by Ruirui, Li, 李锐瑞, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: The effectiveness of keyword-based search engines depends largely on the ability of a user to formulate proper queries that are both expressive and selective. However, 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, namely relevancy, redundancy-free, diversity, ranking and efficiency. 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. Through query construction from the query concepts, we are able to avoid recommendation redundancy. (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 on real search log datasets. Our experiment shows that DQR outperforms the other methods in terms of relevancy, diversity, and ranking performance of the recommendations. At the same time, DQR also achieves high efficiency performance. DOI: 10.5353/th_b4979975 Subjects: Querying (Computer science) Probabilities


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Product Details
  • ISBN-13: 9781361308950
  • Publisher: Open Dissertation Press
  • Publisher Imprint: Open Dissertation Press
  • Height: 279 mm
  • No of Pages: 78
  • Weight: 204 gr
  • ISBN-10: 1361308958
  • Publisher Date: 26 Jan 2017
  • Binding: Paperback
  • Language: English
  • Spine Width: 4 mm
  • Width: 216 mm


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