# US 2017/0256002 A1 · Identifying groups for a social networking system user based on interactions by the user with various groups > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2017/0256002 A1 - Document ID: US20170256002A1 - Status: Published application - Kind: A1 - Application number: 15/601,107 - Filed: 2017-05-22 - Published: 2017-09-07 - Earliest claimed: 2015-06-25 - Assignees: Facebook, Inc. - Applicants: Facebook, Inc. - Inventors: Bradley Ray Green; Li Ju; Yi Miao - Web page: https://patentgrep.com/patent/US20170256002A1 ## CPC - G06Q 10/42: Determination of affinities or common interests between users - G06Q 10/48: using social graphs - H04L 51/52: for supporting social networking services - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract 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 determines scores for various groups representing a likelihood of the user interacting with the groups. The social networking system may identify a set of groups based on interactions between the user and various groups occurring during a specific time interval and determine scores for groups in the set. When determining a score for a group, the social networking system accounts for times associated with interactions between the user and various groups. Based on the scores, one or more groups are selected and presented to the user. Additionally, the social networking system may identify a time to present the selected one or more groups to the user based on prior user interactions with various groups. ## Claims ### Claim 1 A computer-implemented method comprising: storing, by a computing system, information describing a plurality of groups of an online system, each group in the plurality of groups including one or more users of the online system; acquiring, by the computing system, from a requesting user of the online system, a request to identify a set of groups; acquiring, by the computing system, information describing one or more interactions between the requesting user and one or more groups in the plurality of groups; determining, by the computing system, based on applying a model to the one or more interactions, one or more frequencies with which the requesting user performs the one or more interactions with respect to the one or more groups; determining, by the computing system, based on the one or more frequencies, one or more scores for the one or more groups, each of the one or more scores representing a respective likelihood that the requesting user will interact with each of the one or more groups within a specified time interval; identifying, by the computing system, based on the one or more scores, at least one of the one or more groups that satisfies specified criteria; and selecting, by the computing system, the at least one of the one or more groups to be included in the set of groups. ### Claim 2 (depends on 1) The method of claim 1, wherein applying the model to the one or more interactions further comprises: associating one or more weights with the one or more interactions. ### Claim 3 (depends on 2) The method of claim 2, wherein each of the one or more interactions is performed at a respective time, and wherein each of the one or more weights is determined based on the respective time at which each of the one or more interactions is performed. ### Claim 4 (depends on 3) The method of claim 3, wherein the one or more interactions include a first interaction performed at a first time and a second interaction performed at a second time, and wherein the first time is closer to a current time than the second time, wherein the one or more weights include a first weight and a second weight, wherein the first weight is higher than the second weight, and wherein associating the one or more weights with the one or more interactions further comprises: associating the first weight with the first interaction; and associating the second weight with the second interaction. ### Claim 5 (depends on 2) The method of claim 2, wherein each of the one or more interactions is associated with a respective interaction type, and wherein each of the one or more weights is determined based on the respective interaction type associated with each of the one or more interactions. ### Claim 6 (depends on 2) The method of claim 2, wherein at least one of the one or more weights is determined based on a number or a percentage of first characteristics associated with a particular group of the one or more groups that match, within an allowable deviation, second characteristics associated with an additional group. ### Claim 7 (depends on 1) The method of claim 1, wherein the specified criteria is associated with at least one of a topic, a location, a user characteristic, or a connection between the requesting user and another entity in the online system. ### Claim 8 A system comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the system to perform: storing information describing a plurality of groups of an online system, each group in the plurality of groups including one or more users of the online system; acquiring, from a requesting user of the online system, a request to identify a set of groups; acquiring information describing one or more interactions between the requesting user and one or more groups in the plurality of groups; determining, based on applying a model to the one or more interactions, one or more frequencies with which the requesting user performs the one or more interactions with respect to the one or more groups; determining, based on the one or more frequencies, one or more scores for the one or more groups, each of the one or more scores representing a respective likelihood that the requesting user will interact with each of the one or more groups within a specified time interval; identifying, based on the one or more scores, at least one of the one or more groups that satisfies specified criteria; and selecting the at least one of the one or more groups to be included in the set of groups. ### Claim 9 (depends on 8) The system of claim 8, wherein applying the model to the one or more interactions further comprises: associating one or more weights with the one or more interactions. ### Claim 10 (depends on 9) The system of claim 9, wherein each of the one or more interactions is performed at a respective time, and wherein each of the one or more weights is determined based on the respective time at which each of the one or more interactions is performed. ### Claim 11 (depends on 10) The system of claim 10, wherein the one or more interactions include a first interaction performed at a first time and a second interaction performed at a second time, and wherein the first time is closer to a current time than the second time, wherein the one or more weights include a first weight and a second weight, wherein the first weight is higher than the second weight, and wherein associating the one or more weights with the one or more interactions further comprises: associating the first weight with the first interaction; and associating the second weight with the second interaction. ### Claim 12 (depends on 9) The system of claim 9, wherein each of the one or more interactions is associated with a respective interaction type, and wherein each of the one or more weights is determined based on the respective interaction type associated with each of the one or more interactions. ### Claim 13 (depends on 9) The system of claim 9, wherein at least one of the one or more weights is determined based on a number or a percentage of first characteristics associated with a particular group of the one or more groups that match, within an allowable deviation, second characteristics associated with an additional group. ### Claim 14 (depends on 8) The system of claim 8, wherein the specified criteria is associated with at least one of a topic, a location, a user characteristic, or a connection between the requesting user and another entity in the online system. ### Claim 15 A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method comprising: storing information describing a plurality of groups of an online system, each group in the plurality of groups including one or more users of the online system; acquiring, from a requesting user of the online system, a request to identify a set of groups; acquiring information describing one or more interactions between the requesting user and one or more groups in the plurality of groups; determining, based on applying a model to the one or more interactions, one or more frequencies with which the requesting user performs the one or more interactions with respect to the one or more groups; determining, based on the one or more frequencies, one or more scores for the one or more groups, each of the one or more scores representing a respective likelihood that the requesting user will interact with each of the one or more groups within a specified time interval; identifying, based on the one or more scores, at least one of the one or more groups that satisfies specified criteria; and selecting the at least one of the one or more groups to be included in the set of groups. ### Claim 16 (depends on 15) The non-transitory computer-readable storage medium of claim 15, wherein applying the model to the one or more interactions further comprises: associating one or more weights with the one or more interactions. ### Claim 17 (depends on 16) The non-transitory computer-readable storage medium of claim 16, wherein each of the one or more interactions is performed at a respective time, and wherein each of the one or more weights is determined based on the respective time at which each of the one or more interactions is performed. ### Claim 18 (depends on 17) The non-transitory computer-readable storage medium of claim 17, wherein the one or more interactions include a first interaction performed at a first time and a second interaction performed at a second time, and wherein the first time is closer to a current time than the second time, wherein the one or more weights include a first weight and a second weight, wherein the first weight is higher than the second weight, and wherein associating the one or more weights with the one or more interactions further comprises: associating the first weight with the first interaction; and associating the second weight with the second interaction. ### Claim 19 (depends on 16) The non-transitory computer-readable storage medium of claim 16, wherein each of the one or more interactions is associated with a respective interaction type, and wherein each of the one or more weights is determined based on the respective interaction type associated with each of the one or more interactions. ### Claim 20 (depends on 16) The non-transitory computer-readable storage medium of claim 16, wherein at least one of the one or more weights is determined based on a number or a percentage of first characteristics associated with a particular group of the one or more groups that match, within an allowable deviation, second characteristics associated with an additional group.