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Jointly Leveraging Intent and Interaction Signals to Predict User Satisfaction with Slate Recommendations - Spotify Research
Detecting and understanding implicit measures of user satisfaction are essential for enhancing recommendation quality. When users interact with a recommendation system, they leave behind fine grained traces of interaction signals, which contain valuable infor- mation that could help gauging user satisfaction. User interaction with such systems is often motivated by a specific need or intent,... View Article
https://research.atspotify.com/publications/jointly-leveraging-intent-and-interaction-signals-to-predict-user-satisfaction-with-slate-recommendations/
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Predicting Intent Using Activity Logs | Proceedings of the 26th International Conference on World Wide Web Companion
Collaborative and social computing theory, concepts and paradigms
https://dl.acm.org/doi/10.1145/3041021.3054198
Predicting Intent Using Activity Logs
People have different intents in using online platforms. They may be trying to accomplish specific, short-term goals, or less well-defined, longer-term …
https://speakerdeck.com/jcccf/predicting-intent-using-activity-logs
