[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120873-en":3,"doc-seo-120873-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},120873,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Machine Learning Based Identification of Trending Videos by Location and Topic","Video discovery supports user satisfaction on online video hosting and social media platforms, yet popular playlists may not reflect real-time trends within specific regions. This disclosure presents a machine-learning approach that analyzes short-form and other videos to associate one or more geographic locations (e.g., creation or popular-at locations) and to assign topics or categories. Topics can be derived via machine-learning clustering, and combined location-topic information enables personalized trend discovery. A digital map interface supports browsing trending videos by region and beyond worldwide.","Technical Disclosure Commons  \nDefensive Publications Series  \nDecember 2023  \nMachine Learning Based Identification of Trending Videos by Location and Topic  \nJoseph Johnson Jr.  \nQuinn Thuy Tran  \nFollow this and additional works at: [https://www.tdcommons.org/dpubs_series](https://www.tdcommons.org/dpubs_series)  \nRecommended Citation  \nJohnson Jr., Joseph and Tran, Quinn Thuy, \"Machine Learning Based Identification of Trending Videos by Location and Topic\", Technical Disclosure Commons,(December 19, 2023)  \n[https://www.tdcommons.org/dpubs_series/6503](https://www.tdcommons.org/dpubs_series/6503)  \nThis work is licensed under a Creative Commons Attribution 4.0 License.  \nThis Article is brought to you for free and open access by Technical Disclosure Commons. It has been accepted for inclusion in Defensive Publications Series by an authorized administrator of Technical Disclosure Commons.  \nMachine Learning Based Identification of Trending Videos by Location and Topic  \nABSTRACT  \nVideo content discovery is important to provide user satisfaction to users of online video hosting/sharing and social media platforms. In some cases, such platforms may curate  \nautomatic playlists of popular videos. However, such playlists may not accurately reflect  \ncurrently popular trends in particular regions. This disclosure describes the use of machine  \nlearning techniques to automatically analyze videos, including short form videos, to associate one or more locations to the video (e.g., creation location, popular-at locations, etc.) as well as  \nto assign topics or categories to the video (e.g., dance type A, magic trick B, etc.) . The topics  \nmay be discovered based on machine-learning based clustering techniques. The location and  \ntopic/category information are utilized to enable personalized discovery of trends for short  \nvideo content. A digital map based interface may be provided to enable users to discover  \ntrending videos in their region and/or at other regions around the world.  \nKEYWORDS  \n● Trending video  \n● Social media trend  \n● Video recommendation  \n● Video categorization  \n● Short video  \n● Video clustering  \n● Location-based recommendation  \n● Personalized recommendation  \nPublished by Technical Disclosure Commons, 2023 2  \nBACKGROUND  \nMany online video and social media platforms support creation and sharing of videos,  \nincluding short videos (e.g., videos of duration less than 30 seconds, videos of duration less  \nthan 1 minute, etc.) . It can be difficult for users to discover new content available on such  \nplatforms, especially content that is trending in their area. Many platforms offer an automated  \nuser experience, where the videos are played in a back-to-back manner, with the user being  \nprovided options to skip to the next video. Such a user experience may be powered by  \nrecommendation algorithms However, such recommendation algorithms may not generate  \ncontent playlists that include videos for a particular topic or trending in a particular area. There  \nis no current mechanism that offers a way to visualize trends by geography. Automatically  \nclustering or categorizing short videos into topics/categories and inferring geography/location  \nassociated with a video is a hard problem, given the short duration of such videos as well as  \nlack of sufficient metadata.  \nFor example, video hosting/sharing and social media platforms may serve short videos of dances, with many different dance trends being featured across the various videos. Similarly, other videos may include content such as magic and party tricks, parkour and sports moves, public parades or events (e.g., marches or protests), etc. Categorizing such videos based on content/location is difficult, and thereby, automated curation of videos cannot generate high quality content recommendations based on such factors.  \nDESCRIPTION  \nThis disclosure describes the use of machine learning techniques to automatically analyze videos, including short form","cbCaidpfTODkBjKP","https://ap.wps.com/l/cbCaidpfTODkBjKP","pdf",162157,1,"English","en",105,"# Abstract\n# Background\n## Challenges in short-video trend visualization\n## Difficulty of categorizing by content and geography\n# Description\n## Location assignment and inference from activity data\n## Topic/category assignment via machine-learning clustering\n## Geographic and topic-based video recommendation interface","[{\"question\":\"How does the disclosure identify trending videos across regions?\",\"answer\":\"It uses machine learning to analyze videos and user activity data to detect trends by both topic and geography, then supports discovery through the resulting location-topic associations.\"},{\"question\":\"What information is used to associate locations with a video?\",\"answer\":\"Locations can come from available metadata such as user-provided creation or sharing location, and can also be inferred from activity data such as shares and comments tied to user regions.\"},{\"question\":\"How are topics or categories assigned to videos?\",\"answer\":\"Topics or categories are assigned by applying machine-learning clustering techniques to discover groups based on video content, such as categories corresponding to specific types of actions or tricks.\"}]","Machine Learning Based Identification of Trending Videos by Location and Topic | 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does the disclosure identify trending videos across regions?","Question",{"text":74,"@type":75},"It uses machine learning to analyze videos and user activity data to detect trends by both topic and geography, then supports discovery through the resulting location-topic associations.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What information is used to associate locations with a video?",{"text":79,"@type":75},"Locations can come from available metadata such as user-provided creation or sharing location, and can also be inferred from activity data such as shares and comments tied to user regions.",{"name":81,"@type":72,"acceptedAnswer":82},"How are topics or categories assigned to videos?",{"text":83,"@type":75},"Topics or categories are assigned by applying machine-learning clustering techniques to discover groups based on video content, such as categories corresponding to specific types of actions or 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