Amazon Technologies, Inc.

A9.com, Inc.

Michael L Hamrick

Joel R Spiegel

Ruben Ernesto Ortega

Dwayne Edward Bowman

Timothy Richard Kohn

A system is disclosed that identifies related search terms by performing a term co-occurrence analysis of search query submissions of users. The system may give different amounts of weight to different search query submissions based, for example, on whether subsequent user actions reveal that particular searches were successful. The system may use the identified related search terms to correct spelling errors in search queries received from users.

Identifying related search terms based on search behaviors of users

A search engine is disclosed which suggests related terms to the user to allow the user to refine a search. The related terms are generated using query term correlation data which reflects the frequencies with which specific terms have previously appeared within the same query. The correlation data is generated and stored in a look-up table using an off-line process which parses a query log file. The table is regenerated periodically from the most recent query submissions (e.g., the last two weeks of query submissions), and thus strongly reflects the current preferences of users. Each related term is presented to the user via a respective hyperlink which can be selected by the user to submit a modified query. In one embodiment, the related terms are added to and selected from the table so as to guarantee that the modified queries will not produce a NULL query result.

System and method for refining search queries

A search engine is disclosed that uses correlations between search terms to correct misspelled terms within search queries. The correlations are based at least in-part on historical query submissions to the search engine. Preferably, the correlations reflect the frequencies with which the search terms have historically appeared together within the same query, and are stored within a correlation table using related terms lists. In one embodiment, the correlation table is generated periodically from the M (e.g. 10) most recent days of entries in a query log, and thus reflects the current preferences of users. In operation, when a query that includes both matching and non-matching search terms is submitted to the search engine, a spelling correction process accesses the correlation table to generate a list of terms that are deemed to be related to the matching term(s). The spellings of these related terms are then compared to the spelling of each non-matching term using a spelling comparison function that compares two character strings and generates a similarity score. If a suitable replacement is found for a given non-matching term, the non-matching term is replaced with the similar related term. The modified query is then used to perform the search, and the user is notified of the modification(s) made to the query. In the disclosed embodiment, the search engine is used on the Web site of an online merchant to assist users in locating book titles, music titles, and other types of products.

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Seattle, WA, United States of America

Michael L Hamrick