论文标题
协作过滤中层次板块的学习表示
Learning Representations of Hierarchical Slates in Collaborative Filtering
论文作者
论文摘要
我们有兴趣为推荐系统建立协作过滤模型，在这些系统中，用户与板岩而不是单个项目进行交互。这些板岩本质上可以是分层的。我们方法的核心思想是学习这些石板的低维嵌入。我们通过利用生成层次数据的基本分布的（未知）统计数据来介绍一种学习这些嵌入的新方法。我们的表示学习算法可以看作是一个简单的组成规则，可以以自下而上的方式递归地应用，以根据其组成部分的表示来表示任意复杂的层次结构。我们在两个现实世界推荐系统数据集上演示了我们的想法，包括用于Recsys 2019挑战的一个。对于该数据集，我们通过将嵌入作为我们的方法中产生的功能将嵌入到其解决方案中所产生的功能来改进获胜团队的模型所取得的性能。
We are interested in building collaborative filtering models for recommendation systems where users interact with slates instead of individual items. These slates can be hierarchical in nature. The central idea of our approach is to learn low dimensional embeddings of these slates. We present a novel way to learn these embeddings by making use of the (unknown) statistics of the underlying distribution generating the hierarchical data. Our representation learning algorithm can be viewed as a simple composition rule that can be applied recursively in a bottom-up fashion to represent arbitrarily complex hierarchical structures in terms of the representations of its constituent components. We demonstrate our ideas on two real world recommendation systems datasets including the one used for the RecSys 2019 challenge. For that dataset, we improve upon the performance achieved by the winning team's model by incorporating embeddings as features generated by our approach in their solution.
