Explicit Search Result Diversification Using Score and Rank Aggregation Methods

Date

2015-06-01

Author

Ozdemiray, Ahmet Murat
Altıngövde, İsmail Sengör

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Search result diversification is one of the key techniques to cope with the ambiguous and underspecified information needs of web users. In the last few years, strategies that are based on the explicit knowledge of query aspects emerged as highly effective ways of diversifying search results. Our contributions in this article are two-fold. First, we extensively evaluate the performance of a state-of-the-art explicit diversification strategy and pin-point its potential weaknesses. We propose basic yet novel optimizations to remedy these weaknesses and boost the performance of this algorithm. As a second contribution, inspired by the success of the current diversification strategies that exploit the relevance of the candidate documents to individual query aspects, we cast the diversification problem into the problem of ranking aggregation. To this end, we propose to materialize the re-rankings of the candidate documents for each query aspect and then merge these rankings by adapting the score(-based) and rank(-based) aggregation methods. Our extensive experimental evaluations show that certain ranking aggregation methods are superior to existing explicit diversification strategies in terms of diversification effectiveness. Furthermore, these ranking aggregation methods have lower computational complexity than the state-of-the-art diversification strategies.

Subject Keywords

Meta search engines, Information storage and retrieval systems, Search engines

URI

https://hdl.handle.net/11511/42157

Journal

JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY

DOI

https://doi.org/10.1002/asi.23259

Collections

Department of Computer Engineering, Article

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Citation Formats

A. M. Ozdemiray and İ. S. Altıngövde, “Explicit Search Result Diversification Using Score and Rank Aggregation Methods,” JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY, pp. 1212–1228, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42157.