（1）本文针对POI定义了三个概念：Geo-influence (可理解为从该点出发到其他点点可能性); Geo-susceptibility (可以理解为其他点到该点到可能性)； physical distance (物理距离)
（2）然后本文用两个低维矩阵分别刻画了Geo-influence和Geo-susceptibility，用一个geographyical influence function刻画了physical distance
（3）将（2）中三个变量相乘，得到一个标量。每一个unvisited POI同所有visited POI之间都有一个这样的标量，他们的和就可以表达从当前点到该unvisited poi之间的联系强弱。
（4）将（3）中的和同传统的矩阵分解得到的预测值结合（相加）到一起，作为最终的预测值
本文中有一个刻画标量（physical distance）的函数可以借鉴， 见文章Table 2
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Exploiting POI-Specific Geographical Influence for Point-of-Interest Recommendation | |||||||||||
| 文献作者 | Hao Wang | ||||||||||
| 文献发表年限 | 2018 | ||||||||||
| 文献关键字 | |||||||||||
| physical distance; 一个点两个向量；非对称；asymmetric； 时间或者地理距离信号影响 | |||||||||||
| 摘要描述 | |||||||||||
| Point-of-Interest (POI) recommendation, i.e., recommending unvisited POIs for users, is a fundamental problem for location-based social networks. POI recommendation distinguishes itself from traditional item recommendation, e.g., movie recommendation, via geographical influence among POIs. Existing methods model the geographical influence between two POIs as the probability or propensity that the two POIs are co-visited by the same user given their physical distance. These methods assume that geographical influence between POIs is determined by their physical distance, failing to capture the asymmetry of geographical influence and the high variation of geographical influence across POIs. In this paper, we exploit POI-specific geographical influence to improve POI recommendation. We model the geographical influence between two POIs using three factors: the geo-influence of POI, the geo- susceptibility of POI, and their physical distance. Geo-influence captures POI’s capacity at exerting geographical influence to other POIs, and geo- susceptibility reflects POI’s propensity of being geographically influenced by other POIs. Experimental results on two real-world datasets demonstrate that POI-specific geographical influence significantly improves the performance of POI recommendation. | |||||||||||
