People are looking for complementary contexts, such as team members of complementary skills for project team building and/or reading materials of complementary knowledge for effective student learning, to make their behaviors more likely to be successful. Complementarity has been revealed by behavioral sciences as one of the most important factors in decision making. Existing computational models that learn low-dimensional context representations from behavior data have poor scalability and recent network embedding methods only focus on preserving the similarity between the contexts. In this work, we formulate a behavior entry as a set of context items and propose a novel representation learning method, Multi-type Itemset Embedding , to learn the context representations preserving the itemset structures. We propose a measurement of complementarity between context items in the embedding space. Experiments demonstrate both effectiveness and efficiency of the proposed method over the state-of-the-art methods on behavior prediction and context recommendation. We discover that the complementary contexts and similar contexts are significantly different in human behaviors.

中文翻译：

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使用多类型项集嵌入对行为数据的互补性建模

人们正在寻找互补的环境，例如团队成员互补技能用于项目团队建设和/或阅读材料补充知识为了有效的学生学习，使他们的行为更有可能成功。行为科学揭示了互补性是决策中最重要的因素之一。从行为数据中学习低维上下文表示的现有计算模型具有较差的可扩展性，并且最近的网络嵌入方法仅专注于保持上下文之间的相似性。在这项工作中，我们将行为条目制定为一组上下文项，并提出了一种新颖的表示学习方法，多类型项集嵌入，学习保留项目集结构的上下文表示。我们提出一个互补性测量嵌入空间中的上下文项之间。实验证明了所提出的方法在行为预测和上下文推荐方面的最先进方法的有效性和效率。我们发现互补语境和相似语境在人类行为中存在显着差异。