New algorithm revolutionizes accuracy of recommender systems using user behavior data.
User preference modeling from different user behaviors is important for recommender systems. This article introduces a new way to measure preference confidence from various behaviors, improving accuracy. By using a transfer learning algorithm, confidence can be shared between different behaviors, leading to more precise user preference models. The algorithm outperformed other methods in real-world tests.
