ABSTRACT Context-aware systems enable the sensing and analysis of user context in order to provide personalised services. Our study is part of growing research efforts examining how high-dimensional data collected from mobile devices can be utilised to infer users’ dynamic preferences that are learned over time. We suggest novel methods for inferring the category of the item liked in a specific contextual situation, by applying encoder-decoder learners (long short-term memory networks and auto encoders) on mobile sensor data. In these approaches, the encoder-decoder learners reduce the dimensionality of the contextual features to a latent representation which is learned over time. Given new contextual sensor data from a user, the latent patterns discovered from each deep learner is used to predict the liked item’s category in the given context. This can greatly enhance a variety of services, such as mobile online advertising and context-aware recommender systems. We demonstrate our contribution with a point of interest (POI) recommender system in which we label contextual situations with the items’ categories. Empirical results utilising a real world data set of contextual situations derived from mobile phones sensors log show a significant improvement (up to 73% improvement) in prediction accuracy compared with state of the art classification methods.

中文翻译：

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使用深度编码器-解码器学习器推断上下文偏好

摘要 上下文感知系统能够感知和分析用户上下文，以提供个性化服务。我们的研究是不断增长的研究工作的一部分，研究如何利用从移动设备收集的高维数据来推断用户随时间学习的动态偏好。我们建议通过在移动传感器数据上应用编码器-解码器学习器（长短期记忆网络和自动编码器）来推断在特定上下文情况下喜欢的项目的类别的新方法。在这些方法中，编码器-解码器学习器将上下文特征的维数减少为随着时间学习的潜在表示。给定来自用户的新上下文传感器数据，从每个深度学习器发现的潜在模式用于预测给定上下文中喜欢的项目的类别。这可以极大地增强各种服务，例如移动在线广告和上下文感知推荐系统。我们通过兴趣点 (POI) 推荐系统展示了我们的贡献，在该系统中我们用项目的类别标记上下文情况。与最先进的分类方法相比，利用源自手机传感器日志的真实世界情境数据集的实证结果显示，预测精度显着提高（提高了 73%）。