Information service providers often require evidence from multiple, heterogeneous information sources to better characterize users and offer personalized service. In many cases, statistic information (for example, users' profiles) and sequentially dynamic information (for example, logs of interaction with information systems) are two prominent sources that can be combined to achieve optimized results. Previous attempts in combining these two sources mainly exploited models designed for either static or sequential information, but not both. This study aims to fill the gap by proposing a novel joint neural network model that can naturally fit both static and sequential user data. To evaluate the effectiveness of the proposed method, this study uses the problem of at‐risk student prediction as an example where both static data (personal profiles) and sequential data (event logs) are involved. A thorough evaluation was conducted on an open data set, with comparisons to a range of existing approaches including both static and sequential models. The results reveal superb performances of the proposed method. Implications of the findings on further research and applications of joint models are discussed.

中文翻译：

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用于组合异构用户数据源的联合神经网络模型：风险学生预测示例

信息服务提供商通常需要来自多个异构信息源的证据，以更好地表征用户并提供个性化服务。在许多情况下，统计信息（例如，用户的个人资料）和顺序动态信息（例如，与信息系统交互的日志）是可以组合以实现优化结果的两个突出来源。以前将这两种​​来源结合起来的尝试主要利用为静态或顺序信息设计的模型，但不是两者兼而有之。本研究旨在通过提出一种新的联合神经网络模型来填补这一空白，该模型可以自然地拟合静态和顺序用户数据。为了评估所提出方法的有效性，本研究以高危学生预测问题为例，其中涉及静态数据（个人资料）和顺序数据（事件日志）。对开放数据集进行了彻底评估，并与包括静态和顺序模型在内的一系列现有方法进行了比较。结果显示了所提出方法的卓越性能。讨论了这些发现对联合模型进一步研究和应用的影响。