# US 2013/0159243 A1 · Video recommendation based on video co-occurrence statistics > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2013/0159243 A1 - Document ID: US20130159243A1 - Status: Published application - Kind: A1 - Application number: 13/325,369 - Filed: 2011-12-14 - Published: 2013-06-20 - Earliest claimed: 2011-12-14 - Assignees: Google Inc. - Inventors: Li Wei; Kun Zhang; Yu He; Xinmei Cai - Web page: https://patentgrep.com/patent/US20130159243A1 ## CPC - H04N 21/2668: Creating a channel for a dedicated end-user group, e.g. insertion of targeted commercials based on end-user profiles {(information retrieval from the Internet by querying with filtering and personalisation G06F16/9535; arrangements for replacing or switching information during the broadcast H04H20/10; push services over packet-switching network H04L12/1859; adaptation of message content in packet-switching networks H04L51/063)} - G06F 16/735: Filtering based on additional data, e.g. user or group profiles - G06N 5/04: Inference or reasoning models - H04N 21/26258: {for generating a list of items to be played back in a given order, e.g. playlist, or scheduling item distribution according to such list (retrieval of multimedia data based on playlists G06F16/40)} - H04N 21/44226: {on social networks} - H04N 21/4826: {using recommendation lists, e.g. of programmes or channels sorted out according to their score} - G06N 5/00: Computing arrangements using knowledge-based models - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A system and method provides video recommendations for a target video in a video sharing environment. The system selects one or more videos that are on one or more video playlists together with the target video. The video co-occurrence data of the target video associates the target video and another video on one or more same video playlists and frequency of the target video and another video on the video playlists is computed. Based on the video co-occurrence data of the target video, one or more co-occurrence videos are selected and ranked based on the video co-occurrence data of the target video. The system selects one or more videos from the co-occurrence videos as video recommendations for the target video. ## Claims ### Claim 1 A computer method for generating video recommendations for a video in a video sharing environment, comprising: detecting a target video viewed by a user; generating video co-occurrence data of the target video, the video co-occurrence data including information associated with the target video related to one or more other videos on one or more video playlists; selecting one or more co-occurrence videos associated with the target video based on the video co-occurrence data of the target video, wherein a co-occurrence video of the target video is a video on a video playlist containing the target video; ranking the selected co-occurrence videos; and generating one or more video recommendations for the target video based on the ranking of the selected co-occurrence videos. ### Claim 2 (depends on 1) The method of claim 1, wherein generating video co-occurrence data of the target video comprises: identifying one or more video playlists containing the target video; for each identified video playlist, generating one or more video pairs, each video pair containing the target video and another video on the video playlist; and for each video pair, computing the frequency of the video pair on the one or more video playlists. ### Claim 3 (depends on 1) The method of claim 1, wherein generating video co-occurrence data of the target video further comprises: eliminating duplicate video pairs on a video playlist, a duplicate video pair containing two same videos as another video pair. ### Claim 4 (depends on 1) The method of claim 1, wherein one or more video playlists are generated by a user of the video sharing environment. ### Claim 5 (depends on 1) The method of claim 1, wherein one or more video playlists are generated by a network entity of the video sharing environment . ### Claim 6 (depends on 1) The method of claim 1, wherein a video playlist is described by metadata associated with the video playlist, the metadata identifying one or more videos on the video playlist and order of the videos to be played. ### Claim 7 (depends on 1) The method of claim 1, wherein ranking the selected co-occurrence videos comprises ordering the selected co-occurrence videos based on the frequency of each of the co-occurrence videos paired with the target video on the video playlists. ### Claim 8 (depends on 1) The method of claim 1, wherein ranking the selected co-occurrence videos further comprises ordering the selected co-occurrence videos based on the frequency of each of the co-occurrence videos paired with the target video on the video playlists and one or more ranking factors. ### Claim 9 (depends on 1) The method of claim 1, wherein one or more ranking factors include distance between the target video and each of the selected co-occurrence video, the distance between the target video and a co-occurrence video determined based on the order of the target video and the co-occurrence video to be played. ### Claim 10 (depends on 1) The method of claim 1, further comprising ranking the one or more video playlists based on the video co-occurrence data of videos contained on the video playlists. ### Claim 11 A non-transitory computer-readable storage medium storing executable computer program instructions for generating video recommendations for a video in a video sharing environment, the computer program instructions comprising instructions for: detecting a target video viewed by a user; generating video co-occurrence data of the target video, the video co-occurrence data including information associated with the target video related to one or more other videos on one or more video playlists; selecting one or more co-occurrence videos associated with the target video based on the video co-occurrence data of the target video, wherein a co-occurrence video of the target video is a video on a video playlist containing the target video; ranking the selected co-occurrence videos; and generating one or more video recommendations for the target video based on the ranking of the selected co-occurrence videos. ### Claim 12 (depends on 11) The computer-readable storage medium of claim 11, wherein the computer program instructions for generating video co-occurrence data of the target video comprises instructions for: identifying one or more video playlists containing the target video; for each identified video playlist, generating one or more video pairs, each video pair containing the target video and another video on the video playlist; and for each video pair, computing the frequency of the video pair on the one or more video playlists. ### Claim 13 (depends on 11) The computer-readable storage medium of claim 11, wherein the computer program instructions for generating video co-occurrence data of the target video further comprises instructions for: eliminating duplicate video pairs on a video playlist, a duplicate video pair containing two same videos as another video pair. ### Claim 14 (depends on 11) The computer-readable storage medium of claim 11, wherein a video playlist is described by metadata associated with the video playlist, the metadata identifying one or more videos on the video playlist and order of the videos to be played. ### Claim 15 (depends on 1) The computer-readable storage medium of claim 1, wherein the computer program instructions for ranking the selected co-occurrence videos comprises instructions for ordering the selected co-occurrence videos based on the frequency of each of the co-occurrence videos paired with the target video on the video playlists. ### Claim 16 (depends on 1) The computer-readable storage medium of claim 1, wherein the computer program instructions for ranking the selected co-occurrence videos further comprises instructions for ordering the selected co-occurrence videos based on the frequency of each of the co-occurrence videos paired with the target video on the video playlists and one or more ranking factors. ### Claim 17 (depends on 1) The computer-readable storage medium of claim 1, wherein one or more ranking factors include distance between the target video and each of the selected co-occurrence video, the distance between the target video and a co-occurrence video determined based on the order of the target video and the co-occurrence video to be played. ### Claim 18 (depends on 1) The computer-readable storage medium of claim 1, further comprising computer program instructions for ranking the one or more video playlists based on the video co-occurrence data of videos contained on the video playlists. ### Claim 19 A system for generating video recommendations for a video in a video sharing environment, comprising: a non-transitory computer-readable storage medium storing executable computer modules, comprising: a video co-occurrence module for: detecting a target video viewed by a user; and generating video co-occurrence data of the target video, the video co-occurrence data including information associated with the target video related to one or more other videos on one or more video playlists a ranking module for: selecting one or more co-occurrence videos associated with the target video based on the video co-occurrence data of the target video, wherein a co-occurrence video of the target video is a video on a video playlist containing the target video; and ranking the selected co-occurrence videos; a recommendation module for generating one or more video recommendations for the target video based on the ranking of the selected co-occurrence videos; and a computer processor configured to execute the computer modules. ### Claim 20 (depends on 19) The system of claim 19, wherein the video co-occurrence module is further for: identifying one or more video playlists containing the target video; for each identified video playlist, generating one or more video pairs, each video pair containing the target video and another video on the video playlist; and for each video pair, computing the frequency of the video pair on the video playlists.