Effective Query Recommendation with Medoid-based Clustering using a Combination of Query, Click and Result Features

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ESMAEELI GOHARI ELHAM; ZARIFZADEH SAJJAD

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Journal Paper

Paper Information

Journal: Journal of Information Systems and Telecommunication Year:2020 | Volume:8 | Issue:1 (29) Page(s): 33-44

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Information Journal Paper

Title

Effective Query Recommendation with Medoid-based Clustering using a Combination of Query, Click and Result Features

Author(s)

ESMAEELI GOHARI ELHAM | ZARIFZADEH SAJJAD | Issue Writer Certificate

Keywords

Recommendation Systems

Search Engine

Clustering

Query

Click

Abstract

Query recommendation is now an inseparable part of web Search Engines. The goal of Query recommendation is to help users find their intended information by suggesting similar queries that better reflect their information needs. The existing approaches often consider the similarity between queries from one aspect (e. g., similarity with respect to Query text or search result) and do not take into account different lexical, syntactic and semantic templates exist in relevant queries. In this paper, we propose a novel Query recommendation method that uses a comprehensive set of features to find similar queries. We combine Query text and search result features with bipartite graph modeling of user Clicks to measure the similarity between queries. Our method is composed of two separate offline (training) and online (test) phases. In the offline phase, it employs an efficient k-medoids algorithm to cluster queries with a tolerable processing and memory overhead. In the online phase, we devise a randomized nearest neighbor algorithm for identifying most similar queries with a low response-time. Our evaluation results on two separate datasets from AOL and Parsijoo Search Engines show the superiority of the proposed method in improving the precision of Query recommendation, e. g., by more than 20% in terms of p@10, compared with some well-known algorithms.

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APA: Copy

ESMAEELI GOHARI, ELHAM, & ZARIFZADEH, SAJJAD. (2020). Effective Query Recommendation with Medoid-based Clustering using a Combination of Query, Click and Result Features. JOURNAL OF INFORMATION SYSTEMS AND TELECOMMUNICATION (JIST), 8(1 (29)), 33-44. SID. https://sid.ir/paper/332818/en

Vancouver: Copy

ESMAEELI GOHARI ELHAM, ZARIFZADEH SAJJAD. Effective Query Recommendation with Medoid-based Clustering using a Combination of Query, Click and Result Features. JOURNAL OF INFORMATION SYSTEMS AND TELECOMMUNICATION (JIST)[Internet]. 2020;8(1 (29)):33-44. Available from: https://sid.ir/paper/332818/en

IEEE: Copy

ELHAM ESMAEELI GOHARI, and SAJJAD ZARIFZADEH, “Effective Query Recommendation with Medoid-based Clustering using a Combination of Query, Click and Result Features,” JOURNAL OF INFORMATION SYSTEMS AND TELECOMMUNICATION (JIST), vol. 8, no. 1 (29), pp. 33–44, 2020, [Online]. Available: https://sid.ir/paper/332818/en

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