Joint prediction of watch ratio and skip behaviour in recommendation system 

Joint prediction of watch ratio and skip behaviour in recommendation system | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 March 2026 V1 Latest version Share on Joint prediction of watch ratio and skip behaviour in recommendation system Author : Mahdi Rezapour 0000-0002-3368-722X [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177307080.00824897/v1 96 views 62 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This study examines user engagement with online video content using a multi-task learning approach. In this study, we combine viewing histories, basic user attributes, and content datasets from several public sources to predict both the proportion of a video watched and whether a user skips a video. The two tasks are learned jointly, using a shared representation with separate outputs for regression and classification. Several common multi-task architectures are evaluated and compared under the same experimental setup. Techniques like Multi-Gate Mixture-of-Experts (MMoE), and Progressive Layered Extraction (PLE), and cross stick network were employed. Results of this study on a held-out test set show that watch ratio can be predicted with reasonable accuracy, while skip prediction remains challenging and only marginally better than random guessing. Differences between model architectures are small, suggesting that data size and label definition might have a stronger influence on performance than model choice. These findings highlight the difficulty of modeling discrete engagement outcomes from noisy behavioral data and point to the importance of careful label construction in future work. Especially, this study highlights the challenges of prediction of skip prediction due to likely reason of subjectively setting the threshold. Keywords User engagement • watch ratio • skip behavior • multi-task learning • recommender systems • behavioral data • video consumption • user modeling Supplementary Material File (aa603fbe-7fa7-42d7-9770-e8009eed5a1b.pdf) Download 149.54 KB Information & Authors Information Version history V1 Version 1 09 March 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords multi-task learning recommender systems skip behavior user engagement watch ratio Authors Affiliations Mahdi Rezapour 0000-0002-3368-722X [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 96 views 62 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Mahdi Rezapour. Joint prediction of watch ratio and skip behaviour in recommendation system . Authorea . 09 March 2026. DOI: https://doi.org/10.22541/au.177307080.00824897/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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