{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/content-based-video-relevance-prediction","title":"Content-based Video Relevance Prediction Challenge: Data, Protocol, and Baseline","arxiv_id":"1806.00737","date":"2018-06-03","proceeding":null,"authors":["Mengyi Liu","Xiaohui Xie","Hanning Zhou"],"abstract":"Video relevance prediction is one of the most important tasks for online\nstreaming service. Given the relevance of videos and viewer feedbacks, the\nsystem can provide personalized recommendations, which will help the user\ndiscover more content of interest. In most online service, the computation of\nvideo relevance table is based on users' implicit feedback, e.g. watch and\nsearch history. However, this kind of method performs poorly for \"cold-start\"\nproblems - when a new video is added to the library, the recommendation system\nneeds to bootstrap the video relevance score with very little user behavior\nknown. One promising approach to solve it is analyzing video content itself,\ni.e. predicting video relevance by video frame, audio, subtitle and metadata.\nIn this paper, we describe a challenge on Content-based Video Relevance\nPrediction (CBVRP) that is hosted by Hulu in the ACM Multimedia Conference\n2018. In this challenge, Hulu drives the study on an open problem of exploiting\ncontent characteristics directly from original video for video relevance\nprediction. We provide massive video assets and ground truth relevance derived\nfrom our really system, to build up a common platform for algorithm development\nand performance evaluation.","url_abs":"http://arxiv.org/abs/1806.00737v1","url_pdf":"http://arxiv.org/pdf/1806.00737v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"content-based-video-relevance-prediction","repo_url":"https://github.com/mengyi-liu/cbvrp-acmmm-2018","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"content-based-video-relevance-prediction","repo_url":"https://github.com/cbvrp-acmmm-2018/cbvrp-acmmm-2018","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}