# US 10,320,927 B2 · Systems and methods for providing personalized content > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,320,927 B2 - Document ID: US10320927B2 - Status: Granted - Kind: B2 - Application number: 15/299,035 - Filed: 2016-10-20 - Published: 2018-04-26 - Granted: 2019-06-11 - Earliest claimed: 2016-10-20 - Assignees: Facebook, Inc. - Applicants: Facebook, Inc. - Inventors: Chen Zheng; Thomas Frederick Dimson; Linji Yang - Web page: https://patentgrep.com/patent/US10320927B2 ## CPC - H04L 67/06: specially adapted for file transfer, e.g. file transfer protocol [FTP] - G06Q 10/10: Office automation; Time management - G06Q 10/42: Determination of affinities or common interests between users - G06Q 10/48: using social graphs - G06Q 30/0241: Advertisements - H04L 51/10: Multimedia information - H04L 51/52: for supporting social networking services - H04L 65/612: for unicast - H04L 67/535: {Tracking the activity of the user (network monitoring arrangements H04L43/00; recording of computer activity G06F11/34)} - G06N 20/00: Machine learning - H04L 67/52: specially adapted for the location of the user terminal - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) - 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 - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Systems, methods, and non-transitory computer-readable media can generate a set of candidate content items from a plurality of content items that are available in the social networking system, wherein one or more of the candidate content items are to be included in a personalized content stream for a first user. A corresponding score for each of the candidate content items can be generated with respect to the first user. A first set of content items can be determined from the set of candidate content items based at least in part on the respective scores, wherein content items in the first set are included in the personalized content stream. ## Claims ### Claim 1 A computer-implemented method comprising: generating, by a social networking system, a set of candidate content items from a plurality of content items that are available in the social networking system, wherein one or more of the candidate content items are to be included in a personalized content stream for a first user, wherein at least one candidate content item to be included in the personalized content stream is selected based on the at least one candidate content item being posted by a user that is located in a geographic region in which the first user is also located or has visited; generating, by the social networking system, a corresponding score for each of the candidate content items with respect to the first user; and determining, by the social networking system, a first set of content items from the set of candidate content items based at least in part on the respective scores, wherein content items in the first set are included in the personalized content stream. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein generating a respective score for a candidate content item further comprises: determining, by the social networking system, a likelihood of the first user selecting an option to like a candidate content item through the social networking system, the likelihood being determined using a trained machine learning model, wherein the score for the candidate content item is based at least in part on the likelihood. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein generating a respective score for a candidate content item further comprises: determining, by the social networking system, a likelihood of the first user watching one or more additional content items after having viewed a candidate content item, the likelihood being determined using a trained machine learning model, wherein the score for the candidate content item is based at least in part on the likelihood. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein generating a respective score for a candidate content item further comprises: determining, by the social networking system, a likelihood of the first user watching a candidate content item to completion, the likelihood being determined using a trained machine learning model, wherein the score for the candidate content item is based at least in part on the likelihood. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein generating a respective score for a candidate content item further comprises: determining, by the social networking system, a likelihood of the first user watching a playback of a candidate content item for a duration of time that is longer than an average duration of time the first user watches playback of content items, the likelihood being determined using a trained machine learning model, wherein the score for the candidate content item is based at least in part on the likelihood. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein generating a respective score for a candidate content item further comprises: determining, by the social networking system, a likelihood of the first user watching a playback of a candidate content item for a duration of time that is longer than an average duration of time that other users watched playbacks of the candidate content item, the likelihood being determined using a trained machine learning model, wherein the score for the candidate content item is based at least in part on the likelihood. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein generating the set of candidate content items further comprises: obtaining, by the social networking system, one or more content items that were liked by at least one second user that the first user is following in the social networking system. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, wherein generating the set of candidate content items further comprises: determining, by the social networking system, that the first user has previously liked one or more content items that were posted by at least one second user; and obtaining, by the social networking system, one or more content items that were liked by the second user. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein generating the set of candidate content items further comprises: obtaining, by the social networking system, one or more content items that were posted by users that are located in a geographic region in which the first user is also located or has visited. ### Claim 10 (depends on 1) The computer-implemented method of claim 1, the method further comprising: filtering, by the social networking system, the set of candidate content items to exclude content items that are likely to be flagged by users as being inappropriate or content items that were posted by users that have previously been flagged as posters of inappropriate content. ### Claim 11 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: generating a set of candidate content items from a plurality of content items that are available in the social networking system, wherein one or more of the candidate content items are to be included in a personalized content stream for a first user, wherein at least one candidate content item to be included in the personalized content stream is selected based on the at least one candidate content item being posted by a user that is located in a geographic region in which the first user is also located or has visited; generating a corresponding score for each of the candidate content items with respect to the first user; and determining a first set of content items from the set of candidate content items based at least in part on the respective scores, wherein content items in the first set are included in the personalized content stream. ### Claim 12 (depends on 11) The system of claim 11, wherein generating a respective score for a candidate content item further causes the system to perform: determining a likelihood of the first user selecting an option to like a candidate content item through the social networking system, the likelihood being determined using a trained machine learning model, wherein the score for the candidate content item is based at least in part on the likelihood. ### Claim 13 (depends on 11) The system of claim 11, wherein generating a respective score for a candidate content item further causes the system to perform: determining a likelihood of the first user watching one or more additional content items after having viewed a candidate content item, the likelihood being determined using a trained machine learning model, wherein the score for the candidate content item is based at least in part on the likelihood. ### Claim 14 (depends on 11) The system of claim 11, wherein generating a respective score for a candidate content item further causes the system to perform: determining a likelihood of the first user watching a candidate content item to completion, the likelihood being determined using a trained machine learning model, wherein the score for the candidate content item is based at least in part on the likelihood. ### Claim 15 (depends on 11) The system of claim 11, wherein generating a respective score for a candidate content item further causes the system to perform: determining a likelihood of the first user watching a playback of a candidate content item for a duration of time that is longer than an average duration of time the first user watches playback of content items, the likelihood being determined using a trained machine learning model, wherein the score for the candidate content item is based at least in part on the likelihood. ### Claim 16 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: generating a set of candidate content items from a plurality of content items that are available in the social networking system, wherein one or more of the candidate content items are to be included in a personalized content stream for a first user, wherein at least one candidate content item to be included in the personalized content stream is selected based on the at least one candidate content item being posted by a user that is located in a geographic region in which the first user is also located or has visited; generating a corresponding score for each of the candidate content items with respect to the first user; and determining a first set of content items from the set of candidate content items based at least in part on the respective scores, wherein content items in the first set are included in the personalized content stream. ### Claim 17 (depends on 16) The non-transitory computer-readable storage medium of claim 16, wherein generating a respective score for a candidate content item further causes the computing system to perform: determining a likelihood of the first user selecting an option to like a candidate content item through the social networking system, the likelihood being determined using a trained machine learning model, wherein the score for the candidate content item is based at least in part on the likelihood. ### Claim 18 (depends on 16) The non-transitory computer-readable storage medium of claim 16, wherein generating a respective score for a candidate content item further causes the computing system to perform: determining a likelihood of the first user watching one or more additional content items after having viewed a candidate content item, the likelihood being determined using a trained machine learning model, wherein the score for the candidate content item is based at least in part on the likelihood. ### Claim 19 (depends on 16) The non-transitory computer-readable storage medium of claim 16, wherein generating a respective score for a candidate content item further causes the computing system to perform: determining a likelihood of the first user watching a candidate content item to completion, the likelihood being determined using a trained machine learning model, wherein the score for the candidate content item is based at least in part on the likelihood. ### Claim 20 (depends on 16) The non-transitory computer-readable storage medium of claim 16, wherein generating a respective score for a candidate content item further causes the computing system to perform: determining a likelihood of the first user watching a playback of a candidate content item for a duration of time that is longer than an average duration of time the first user watches playback of content items, the likelihood being determined using a trained machine learning model, wherein the score for the candidate content item is based at least in part on the likelihood.