Facebook, Inc.

Melissa Rose Winstanley

Veselin S Stoyanov

Eric Ringger

Shiun-Zu Kuo

David M Goldblatt

Rose Marie Philip

Daniel Cabral Francisco

Krishna Jayaram Kalpathy

Evan John Rocha

Prerna Totla

Brinda Mehta

In one embodiment, a method includes receiving a search query and parsing the search query to identify one or more terms to optionalize, searching one or more data stores to identify a plurality of objects, including a second set of objects associated with the online social network that matches one or more of the search terms and does not match at least one of the optionalized terms, scoring each of the identified objects, where for each object of the second set of objects the score calculated by the scoring algorithm is based on one or more fuzzy matching terms of the object corresponding to one or more optionalized terms of the search query, respectively, and sending a search-results page to the client system of the first user.

Optionalization and fuzzy search on online social networks

In one embodiment, a method includes receiving a search query inputted by a first user, wherein the search query comprises one or more n-grams; calculating a needle-confidence score for the search query that is calculated by a needle-intent classifier based on at least the n-grams of the search query and a language model analysis of the n-grams, and wherein the needle-confidence score represents a probability that the search query was intended as a needle search; classifying the search query as a needle search if the calculated needle-confidence score is above a threshold confidence score; and generating a plurality of search-result modules, each search-result module comprising one or more search results matching the search query, wherein one of the search-result modules is a social module, and wherein the number of search results in the social module is based on the classification of the search query as a needle search.

Classifying search queries on online social networks

In one embodiment, a method includes receiving a search query from a client system of a first user; parsing the search query into n-grams, and calculating confidence-scores for entities in a set of query-analysis-entities based on the n-grams, each confidence-score representing a probability that one or more of the n-grams are intended to reference a respective entity; determining a set of search results matching the search query, and calculating entity-frequencies corresponding to entities in a set of results-analysis-entities based on a histogram analysis of the set of search results; calculating a filter-score entities in a set of prospective-entities, which includes entities from the set of query-analysis-entities and the set of results-analysis-entities, based on the respective confidence-score and entity-frequency; and sending, to the client system, suggested filters corresponding to entities having a filter-score greater than a threshold filter-score, the suggested filters being selectable to modify the set of search results.

Suggesting filters for search on online social networks

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Seattle, WA, United States of America

Melissa Rose Winstanley