基于域切换感知的整体递归神经网络多域用户行为建模

Donghyun Kim, Sungchul Kim, Handong Zhao, Sheng Li, Ryan A. Rossi, Eunyee Koh
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引用次数: 12

摘要

了解用户行为并预测未来的网络行为对于提供无缝的用户体验以及增加服务提供商的收入至关重要。近年来,由于递归神经网络(RNNs)的显著成功,它已被广泛用于用户行为序列的建模。然而,尽管序列行为在实践中出现在多个领域,但现有的基于rnn的方法仍然侧重于单域场景,假设序列行为仅来自单个领域。因此,为了分析跨多个领域的顺序行为,需要分别训练多个RNN模型,而这些模型无法联合建模跨多个领域的顺序行为之间的相互作用。因此,他们经常受到缺乏每个领域内信息的困扰。在本文中,我们首先介绍了在跨多个领域的连续行为中一个实际但被忽视的现象,即两个连续行为属于不同领域的领域切换。在此基础上,提出了基于域切换感知的整体递归神经网络(DS-HRNN),该网络通过系统地处理多域场景下的域切换,有效地共享从多域提取的知识。DS-HRNN联合多域序列行为建模,仅用一个RNN模型就能准确预测每个域的未来行为。我们对两个真实世界数据集的广泛评估表明,在未来行为预测的召回率方面,DCHRNN\优于现有的基于rnn的方法和非顺序基线,显著提高了14.93%。
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Domain Switch-Aware Holistic Recurrent Neural Network for Modeling Multi-Domain User Behavior
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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