（1）组合用户、POI、Corresponding decision context的一些特征，形成特征集合
（2）为每一个特征学出一个表达
（3）利用scalar projection学出每个特征之间的“weight”（不同于一起直接利用距离，这里用了一个scalar projection，多除了一个向量的模），即self projection attention。
（4）让后利用（3）种的“weights”，为每个特征学出一个新的表达
（5）这个新的表达和原来的表达，在NN的作用下，学出一个各个特征对于decision决策的重要程度。
（6）利用上述的重要程度，同原始的特征表达矩阵组合成一个向量（所谓最后的决策向量）
（7）然后让（6）中 的向量尽可能的同所有特征向量之和尽可能接近。（这里有意思的是，利用的向量的加性，从几何角度出发）
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| WhyWe Go Where We Go: Profiling User Decisions on Choosing POIs | |||||||||||
| 文献作者 | Renjun Hu; Xinjiang Lu | ||||||||||
| 文献发表年限 | 2019 | ||||||||||
| 文献关键字 | |||||||||||
| scalar projection； 一个利用scalar projection做的attention方法 | |||||||||||
| 摘要描述 | |||||||||||
| While Point-of-Interest (POI) recommendation has been a popular topic of study for some time, little progress has been made for understanding why and how people make their decisions for the selection of POIs. To this end, in this paper, we propose a user decision profiling framework, named PROUD, which can identify the key factors in people’s decisions on choosing POIs. Specifically, we treat each user decision as a set of factors and provide a method for learning factor embeddings. A unique perspective of our approach is to seamlessly identify key factors, while preserving decision structures, by maximizing the sum of scalar projection of all related factor embeddings on the aggregated embedding of key factors. In addition, we show that this objective involves nonconvex quadratically constrained quadratic programming (QCQP), which remains NP-hard in general. To address this, our PROUD adopts a self projection attention and an L2 regularized sparse activation to directly estimate the likelihood of each factor to be a key factor. Finally, extensive experiments on real-world data validate the advantage of PROUD in preserving user decision structures. Also, our case study indicates that the identified key decision factors can help us to provide more interpretable recommendations and analysis. | |||||||||||
