Search (3 results, page 1 of 1)

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Abstract

The advent of the Web in the mid-1990s followed by its fast adoption in a relatively short time, posed significant challenges to classical information retrieval methods developed in the 1970s and the 1980s. The major challenges include that the Web is massive, dynamic, and distributed. The two main types of tasks that are carried on the Web are searching and mining. Searching is locating information given an information need, and mining is extracting information and/or knowledge from a corpus. The metrics for success when carrying these tasks on the Web include precision, recall (completeness), freshness, and efficiency.

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Abstract

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.

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Date

16. 1.2016 10:22:28