A machine learning, approach for result caching in web search engines

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Kucukyilmaz T., Cambazoglu B. B., AYKANAT C., Baeza-Yates R.

INFORMATION PROCESSING & MANAGEMENT, vol.53, no.4, pp.834-850, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 53 Issue: 4
  • Publication Date: 2017
  • Doi Number: 10.1016/j.ipm.2017.02.006
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Page Numbers: pp.834-850
  • Keywords: Query result caching, Machine learning, Feature-based caching, Static caching, Static-dynamic caching
  • TED University Affiliated: Yes


A commonly used technique for improving search engine performance is result caching. In result caching, precomputed results (e.g., URLs and snippets of best matching pages) of certain queries are stored in a fast-access storage. The future occurrences of a query whose results are already stored in the cache can be directly served by the result cache, eliminating the need to process the query using costly computing resources. Although other performance metrics are possible, the main performance metric for evaluating the success of a result cache is hit rate. In this work, we present a machine learning approach to improve the hit rate of a result cache by facilitating a large number of features extracted from search engine query logs. We then apply the proposed machine learning approach to static, dynamic, and static-dynamic caching. Compared to the previous methods in the literature, the proposed approach improves the hit rate of the result cache up to 0.66%, which corresponds to 9.60% of the potential room for improvement. (C) 2017 Elsevier Ltd. All rights reserved.