Traditional recommender systems struggle to adapt to users’ changing preferences due to their reliance on single-task learning. This research proposes a multi-task learning (MTL) approach that jointly addresses personalized rating prediction, contextual tag recommendation, and temporal preference modeling. By using shared embeddings and task-specific branches, the model better captures dynamic user behavior and context, improving recommendation accuracy and relevance.
Security and cryptocurrency wallet use are still at the center of the issues with blockchain adoption. Seed phrase-based physical wallets…
The existing traditional e-commerce systems struggle to focus the user query to give a relevant product recommendation at the end…
“The wristGuard application employs a hybrid approach, combining deep learning models for feature extraction and stacking ensemble method for classification…
“The COVID-19 epidemic changed IT industry processes by hastening the introduction of remote work. In virtual workplaces, detecting employee engagement…
“VRoxel is an innovative procedural generation system that addresses critical challenges in creating large-scale 3D environments for VR applications. The…
Block-based programming (BBP) has proven effective in teaching programming concepts, offering a visual and more intuitive approach than traditional text-based…
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