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

Published: 30 January 2019 Publication History

Abstract

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 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 aDomain Switch-Aware Holistic Recurrent Neural Network (DS-HRNN) that effectively shares the knowledge extracted from multiple domains by systematically handlingdomain 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 the future behavior prediction.

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  • (2023)One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain RecommendationProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570379(366-374)Online publication date: 27-Feb-2023
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      cover image ACM Conferences
      WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining
      January 2019
      874 pages
      ISBN:9781450359405
      DOI:10.1145/3289600
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      Published: 30 January 2019

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      Author Tags

      1. domain switch
      2. multi-domain user behavior
      3. recurrent neural network
      4. sequence modeling

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      WSDM '19 Paper Acceptance Rate 84 of 511 submissions, 16%;
      Overall Acceptance Rate 498 of 2,863 submissions, 17%

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      View all
      • (2023)One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain RecommendationProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570379(366-374)Online publication date: 27-Feb-2023
      • (2022)A Comparative Analysis of Human Behavior Prediction Approaches in Intelligent EnvironmentsSensors10.3390/s2203070122:3(701)Online publication date: 18-Jan-2022
      • (2022)Jointly Predicting Future Content in Multiple Social Media Sites Based on Multi-task LearningACM Transactions on Information Systems10.1145/349553040:4(1-28)Online publication date: 11-Jan-2022
      • (2022)Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00265(2942-2955)Online publication date: May-2022
      • (2022)Unsupervised Learning of Domain-Independent User AttributesIEEE Access10.1109/ACCESS.2022.322078110(119649-119665)Online publication date: 2022
      • (2021)Behavior Modeling for a Beacon-Based Indoor Location SystemSensors10.3390/s2114483921:14(4839)Online publication date: 15-Jul-2021
      • (2020)Context-aware Graph Embedding for Session-based News RecommendationProceedings of the 14th ACM Conference on Recommender Systems10.1145/3383313.3418477(657-662)Online publication date: 22-Sep-2020
      • (2020)I Don’t Have That Much Data! Reusing User Behavior Models for Websites from Different DomainsWeb Engineering10.1007/978-3-030-50578-3_11(146-162)Online publication date: 9-Jun-2020
      • (2019)Towards Robust and Discriminative Sequential Data LearningProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330957(1665-1673)Online publication date: 25-Jul-2019

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