Facebook, Inc.

Microsoft Corporation

Eric Ringger

Hetunandan Munisharan Kamichetty

Robert C Moore

Michael Gamon

Rajat Raina

Melissa Rose Winstanley

David M Goldblatt

Aaron Jaech

Brinda Mehta

Daniel Cabral Francisco

Krishna Jayaram Kalpathy

Prerna Totla

Charles Lester Clarke

Martine Smets

Evan John Rocha

Simon Corston-Oliver

In one embodiment, a method includes receiving, from a client system associated with a user, a search query comprising a number of query terms, generating a query match-matrix for the search query, identifying a number of objects matching the search query, retrieving, for each identified object, an object match-matrix for the identified object, constructing, for each identified object, a three-dimensional tensor for the identified object, computing, for each identified object, a relevance score based on the tensor for the identified object, ranking the identified objects based on their respective relevance scores, and sending, to the first client system in response to the search query, instructions for generating a search-results interface for presentation to the user.

Tensor-based deep relevance model for search on online social networks

In one embodiment, a method includes retrieving multiple posts containing each of the query terms after receiving a search query for posts from a user of an online social network, identifying, from an index of known phrases, one or more known phrases in the text of the retrieved posts, filtering the retrieved posts to generate a filtered set of posts, wherein the filtering comprises removing each post not containing at least one of the identified known phrases, computing features for each of the identified known phrases, calculating a score for each post in the filtered set based on the features of the identified known phrases, ranking the posts based on the calculated scores, and sending instructions, to the client system, for generating a search-results interface comprising references to one or more of the posts presented in ranked order.

Measuring phrase association 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

Growing community of inventors

Seattle, WA, United States of America

Eric Ringger