Facebook, Inc.

Meta Platforms, Inc.

Jireh Yiwei Tan

Bradley Ray Green

David Ian Ferguson

Li Ju

Stephen Moore Davis

Brian Paihan Sa

Jun Sun

Nikola Vilimonovic

Aaron Payne Goldsmid

Xun Gong

Kyle Philip Meyer

Pooja Jain

Yi Miao

Jerry Ju

Chen Wang

In one embodiment, a method includes receiving a first query comprising one or more n-grams inputted by the first user at the client system, accessing by the client system a plurality of ideograms each being associated with one or more tags matching at least one of the n-grams of the first query, where the one or more ideograms are ranked in an order based on their respective user-probabilities each being calculated based at least in part on a frequency of use associated with the respective ideogram, presenting one or more of the ideograms at the client system in ranked order where each ideogram is selectable by the first user, and receiving a selection of one of the presented ideograms at the client system.

Searching for ideograms in an online social network

A social networking system selects a set of groups for presentation to a user of the social networking system. To select groups, the social networking system determining scores for various groups representing a likelihood of the user interacting with the groups. When determining a score for a group, the social networking system accounts for a likelihood of the user providing content to the group as well as the user accessing or viewing content associated with the group. Based on the scores, one or more groups are selected and presented to the user. Additionally, the social networking system may apply one or more diversity rules so that the selected groups have a variety of characteristics.

Identifying groups for a social networking system user based on group characteristics and likelihood of user interaction

In one embodiment, a method includes receiving a first query inputted by a first user at a first client system, the first query including one or more n-grams; identifying ideograms, each ideogram being associated with one or more tags, each identified ideogram being associated with at least one tag matching at least one of the n-grams of the received first query; calculating, for each identified ideogram, a use-probability for the ideogram given the received first query, wherein the use-probability is based at least in part on a frequency of use associated with the ideogram; and sending, to the first client system, instructions for presenting a first set of ideograms comprising one or more of the identified ideograms, the first set being determined based on the calculated use-probabilities associated with the ideograms.

A social networking system selects a set of groups for presentation to a user of the social networking system. To select groups, the social networking system identifies candidate groups and selects the set of groups from the candidate groups. To identify certain candidate groups, the social networking system determines a location associated with various groups based on locations associated with users included in the group. For example, the social networking system determines a centroid of a group based on locations associated with users included in the group and associates the centroid with the group if at least a threshold percentage of distances between locations associated with users included in the group and the centroid do not exceed a threshold distance. Groups associated with locations within a threshold distance of a location associated with the user are identified as candidate groups.

Identifying groups for recommendation to a social networking system user based on user location and locations associated with groups

Identifying groups for a social networking system user based on likelihoods of the user interacting with various groups

In one embodiment, a method includes receiving a query inputted at a mobile device of a user of an online social network. The query may include n-grams (e.g., words, emoticons, and punctuation marks), and may be, for example, a message to another user or search terms. N-grams may be mapped to tags, which may, in turn, be mapped to ideograms (i.e., stickers). One or more ideograms may be identified based on the one or more ideogram being associated with tags matching n-grams of the query. For each of the identified ideograms, a probability quantifying how likely the user is to select the ideogram is calculated based on how frequently the ideogram has been used. A set of ideogram suggestions, including one or more of the identified ideograms, is generated based on the probabilities and sent to the user's mobile device for display to the user.

Growing community of inventors

San Francisco, CA, United States of America

Jireh Yiwei Tan