论文标题
基于时间的个性化和推荐系统的序列模型
Time-based Sequence Model for Personalization and Recommendation Systems
论文作者
论文摘要
在本文中，我们开发了一个新颖的推荐模型，该模型明确结合了时间信息。该模型依赖于不同矢量空间中内部产物的嵌入层和TSL注意力样机制，可以将其视为多头注意的修改。该机制使模型可以有效地处理不同长度不同的用户行为序列。我们研究了最先进的模型在统计设计的数据集上的属性。另外，我们表明，它的表现要优于淘杯用户行为数据集上更长的序列长度的更复杂的模型。
In this paper we develop a novel recommendation model that explicitly incorporates time information. The model relies on an embedding layer and TSL attention-like mechanism with inner products in different vector spaces, that can be thought of as a modification of multi-headed attention. This mechanism allows the model to efficiently treat sequences of user behavior of different length. We study the properties of our state-of-the-art model on statistically designed data set. Also, we show that it outperforms more complex models with longer sequence length on the Taobao User Behavior dataset.
