Domain Switch-Aware Holistic Recurrent Neural Network for Modeling Multi-Domain User Behavior

WSDM '19 Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

Publication date: February 11, 2019

Donghyun Kim, Sungchul Kim, Handong Zhao, Sheng Li, Ryan A. Rossi, Eunyee Koh

Understanding user behavior and predicting future behavior on the web is critical for providing seamless user experiences as well as increasing revenue of service providers. Recently, thanks to the remarkable success of recurrent neural networks (RNNs), it has been widely used for modeling sequences of user behaviors. However, although sequential behaviors appear across multiple domains in practice, existing RNN-based approaches still focus on the single-domain scenario assuming that sequential behaviors come from only a single domain. Hence, in order to analyze sequential behaviors across multiple domains, they require to separately train multiple RNN models, which fails to jointly model the interplay among sequential behaviors across multiple domains. Consequently, they often suffer from a lack of information within each domain. In this paper, we first introduce a practical but overlooked phenomenon in sequential behaviors across multiple domains, i.e., domain switch where two successive behaviors belong to different domains. Then, we propose a Domain Switch-Aware Holistic Recurrent Neural Network (DS-HRNN) that effectively shares the knowledge extracted from multiple domains by systematically handling domain switch for the multi-domain scenario. DS-HRNN jointly models the multi-domain sequential behaviors and accurately predicts the future behaviors in each domain with only a single RNN model. Our extensive evaluations on two real-world datasets demonstrate that \DCHRNN\ outperforms existing RNN-based approaches and non-sequential baselines with significant improvements by up to 14.93% in terms of recall of future behavior prediction.

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Research Area:  Adobe Research iconAI & Machine Learning