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Recommendation models are typically trained on datasets constructed from user feedback, which includes both the active feedback (e.g., clicking the Like or Skip buttons) and passive feedback (e.g., auto-play), with passive feedback comprising the majority.
In this paper, we naturally propose modeling user attention prediction as a positive-unlabeled (PU) learning problem, where active feedback is treated as ...
The learned attention model can be used to quantify the reliability of the passive training samples for the downstream music recommendation task, and thus can ...
Music has an increasing impact on people's daily lives, and a sterling music recommendation algorithm can help users find their habitual music accurately.
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User modeling in music recommendation aims to analyze and simulate the user behaviors in music services, which can help im- prove the performance of music ...
This paper proposes a music recommendation model based on multilayer attention representation, which learns song representations from multidimensions.
In this paper, we propose a music recommendation model based on users' long-term and short-term preferences and music emotional attention.
Aug 16, 2024 · We present a music recommendation ranking system that uses Transformer models to better understand the sequential nature of user actions ...
The recommended songs are played automatically although users may not pay any attention to them, posing a challenge of user attention bias in training ...
We propose a Personality and Emotion Integrated Attentive model (PEIA), which fully utilizes social media data to comprehensively model users' long-term taste.