Facebook, Inc.

Hetunandan Munisharan Kamichetty

Eric Ringger

Rajat Raina

Aaron Jaech

Charles Lester Clarke

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.

Growing community of inventors

Seattle, WA, United States of America

Hetunandan Munisharan Kamichetty