[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122746-en":3,"doc-seo-122746-105":30,"detail-sidebar-cat-0-en-105":91},{"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":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122746,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",6,"Technology","SYSTEMS AND METHODS FOR GENERATING CONTENT ITEMS VIA MACHINE LEARNING - Technological field and brief summary","Systems and methods generate a content model and recommendations tied to an online presence. The approach receives indications of user inputs, such as interactions with posts, photos, videos, websites, online shops, reels, or stories, then extracts interests from the received content. A machine learning module derives a set of characteristics and learns associations between user interaction with one or more content items and those characteristics. Recommendations may include product or content promotions and can be delivered via advertisements, profiles, or other online presence generated through machine learning.","Technical Disclosure Commons  \nDefensive Publications Series  \nJuly 2023  \nSYSTEMS AND METHODS FOR GENERATING CONTENT ITEMS VIA MACHINE LEARNING  \nFollow this and additional works at: [https://www.tdcommons.org/dpubs_series](https://www.tdcommons.org/dpubs_series)  \nRecommended Citation  \n\"SYSTEMS AND METHODS FOR GENERATING CONTENT ITEMS VIA MACHINE LEARNING\", Technical Disclosure Commons,(July 27, 2023)  \n[https://www.tdcommons.org/dpubs_series/6080](https://www.tdcommons.org/dpubs_series/6080)  \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.  \nSYSTEMS AND METHODS FOR GENERATING CONTENT ITEMS VIA MACHINE  \nLEARNING  \nTECHNOLOGICAL FIELD  \n[0001] The present disclosure generally relates to systems and methods for generating  \ncontent.  \nBACKGROUND  \n[0002] An online presence may include content interacted with or posted by a user on a  \nplatform. Such content may include products and items associated with a brand, an activity of  \ninterest, and the like. Such information may be useful to advertisers, especially when an  \nadvertised product may relate to one or more items featured in content that was posted or  \ninteracted with. Further, advertisers are increasingly soliciting platforms with a large number of users to promote their products or content. Knowing the association between the online presence  \nof an advertisers target audience and the product or content may be an important criterion for  \nadvertisers. However, current advertising technology may face significant challenges for  \nadvertisers with respect to determining such associations, and otherwise using an online presence  \nto identify potential products of interest to specific users.  \nBRIEF SUMMARY  \n[0003] Various systems, methods, and devices are described for generating a content model and recommendations in association with an online presence. In some examples, the content model may be an advertisement model or any other suitable content model for recommending content items. Recommendations may include product recommendations, presented as content (e.g., an advertisement(s) (e.g., product ads, content ads, or the like)) or other promotion to a user, an online profile, or any other suitable type of online presence which may be generated by machine learning.  \n[0004] In various examples, systems and methods may receive an indication of a user’s input associated with the user, such as interactions with a post(s), photo(s), video(s), website(s), online shop(s), reel(s), or one or more stories. An interest extraction module may determine an interest  \nassociated with the received input. A machine learning module may develop a set of  \nPublished by Technical Disclosure Commons, 2023 2  \ncharacteristics associated with the interest (e.g., user frequently interacts with content associated with shoes). The machine learning model may utilize a neural network to develop an association  \nbetween user interaction with a first content item(s) (e.g., a post, a video, a photo, reel, story, a  \nwebsite, an online marketplace, an online shop, or any suitable content item(s) or combination  \nthereof) and a characteristic. A recommendation may be generated based on an association  \nbetween the set of characteristics and the content item(s). A machine learning module, which  \nmay be the same or different machine learning module may generate the recommendation. The  \ncontent items may include content associated with a platform (e.g., an interactive platform) . In  \nvarious examples, a report identifying a user profile, of the online presence may assist in  \nidentifying targeted user-generated content (UGC) or content for promotion (e.g., a  \nrecommendation) via an interactive platform.  \n[0005] In various examples, the interest extracti","cbCaijB3cAjzL2HP","https://ap.wps.com/l/cbCaijB3cAjzL2HP","pdf",1291403,1,37,"English","en",105,"# Technological Field\n## Background\n## Brief Summary\n## Interest Extraction and Characteristics\n## Recommendation Generation","[{\"question\":\"What inputs does the system use to generate content item recommendations?\",\"answer\":\"The system receives user-related input indications such as interactions with posts, photos, videos, websites, online shops, reels, stories, and similar online content.\"},{\"question\":\"How does the system extract user interests and transform them into usable data?\",\"answer\":\"An interest extraction module determines interests associated with the input, such as clothing items, brands, activities, items, sounds, songs, foods, scenes, individuals, and stores, then derives characteristics related to those interests.\"},{\"question\":\"How are recommendations generated and delivered to users or content providers?\",\"answer\":\"A machine learning module learns associations between interaction with content items and the extracted characteristics, then generates recommendations that may be presented as ads, profile-based promotions, or other online presence outputs via device interfaces.\"}]","SYSTEMS AND METHODS FOR GENERATING CONTENT ITEMS VIA MACHINE LEARNING - 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