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Predicting user behavior on video streaming by using watch-time duration analysis
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DOI：10.1016/j.knosys.2025.114779.png)
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Abstract
En 中文
• Introduce CBCB with sequential (CBCB-S) and revert (CBCB-R) behaviours. • Leverage watch-time duration with user history for short-term prediction. • Outperform VideoReach and UVCAN on Precision, Recall, F1, and Accuracy. • CBCB-R achieves Recall 1.000 and F1-Score 0.990 on JAWWY logs. • Decision Tree is the strongest conventional baseline across datasets.
Keywords:
Short-term sequential behavior
Personalized video recommendation
Content-based filtering
Watch-time behavior
Machhine learning
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