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首页> 外文期刊>IEEE Transactions on Knowledge and Data Engineering >Learning to Recommend With Multiple Cascading Behaviors
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Learning to Recommend With Multiple Cascading Behaviors

机译：学习推荐使用多个级联行为

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摘要

Most existing recommender systems leverage user behavior data of one type only, such as the purchase behavior in E-commerce that is directly related to the business Key Performance Indicator (KPI) of conversion rate. Besides the key behavioral data, we argue that other forms of user behaviors also provide valuable signal, such as views, clicks, adding a product to shopping carts and so on. They should be taken into account properly to provide quality recommendation for users. In this work, we contribute a new solution named short for Neural Multi-Task Recommendation (NMTR) for learning recommender systems from user multi-behavior data. We develop a neural network model to capture the complicated and multi-type interactions between users and items. In particular, our model accounts for the cascading relationship among different types of behaviors (e.g., a user must click on a product before purchasing it). To fully exploit the signal in the data of multiple types of behaviors, we perform a joint optimization based on the multi-task learning framework, where the optimization on a behavior is treated as a task. Extensive experiments on two real-world datasets demonstrate that NMTR significantly outperforms state-of-the-art recommender systems that are designed to learn from both single-behavior data and multi-behavior data. Further analysis shows that modeling multiple behaviors is particularly useful for providing recommendation for sparse users that have very few interactions.
机译：大多数现有推荐系统仅利用了一个类型的用户行为数据，例如电子商务中的购买行为与转换率的业务密钥性能指示符（KPI）直接相关。除了关键的行为数据外，我们认为其他形式的用户行为还提供有价值的信号，例如视图，点击，将产品添加到购物车等。应妥善考虑，以便为用户提供质量推荐。在这项工作中，我们为来自用户多行为数据学习推荐系统的神经多任务推荐（NMTR）提供了一个名为Night的新解决方案。我们开发了一个神经网络模型，以捕获用户和项目之间的复杂和多型交互。特别是，我们的模型考虑了不同类型行为之间的级联关系（例如，用户必须在购买之前单击产品）。为了充分利用多种行为数据中的信号，我们基于多任务学习框架执行联合优化，其中对行为的优化被视为任务。两个实际数据集的广泛实验表明NMTR显着优于最先进的推荐系统，这些系统旨在从单行为数据和多行为数据中学习。进一步的分析表明，建模多种行为对于为稀疏用户提供具有很少的交互的推荐特别有用。

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