JUMP: a Jointly Predictor for User Click and Dwell Time
JUMP: a Jointly Predictor for User Click and Dwell Time
Tengfei Zhou, Hui Qian, Zebang Shen, Chao Zhang, Chengwei Wang, Shichen Liu, Wenwu Ou
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 3704-3710.
https://doi.org/10.24963/ijcai.2018/515
With the recent proliferation of recommendation system, there have been a lot of interests in session-based prediction methods, particularly those based on Recurrent Neural Network (RNN) and their variants. However, existing methods either ignore the dwell time prediction that plays an important role in measuring user's engagement on the content, or fail to process very short or noisy sessions. In this paper, we propose a joint predictor, JUMP, for both user click and dwell time in session-based settings. To map its input into a feature vector, JUMP adopts a novel three-layered RNN structure which includes a fast-slow layer for very short sessions and an attention layer for noisy sessions. Experiments demonstrate that JUMP outperforms state-of-the-art methods in both user click and dwell time prediction.
Keywords:
Machine Learning: Recommender Systems
Machine Learning Applications: Applications of Supervised Learning