TaxiRec （阅读笔记）20180119
《TaxiRec: Recommending Road Clusters to Taxi Drivers Using Ranking-based Extreme Learning Machines》ACM SIGSPATIAL 2015
1、Abstract
Propose TaxiRec: a framework for discovering the passenger-finding potentials of road clusters, which is incorporated into a recommender system for taxi drivers to hunt passengers.（提出了一个出租车寻客推荐模型）
In TaxiRec, we first construct the road network by defining the nodes and road segments. （根据nodes和road segement 构建路网）
Then, the road network is divided into a number of road clusters through a clustering process on the mid points of the road segments.（将路网分割为一系列的clusters）
a set of features for each road cluster is extracted from real-life data sets, and a ranking-based extreme learning machine (ELM) model is proposed to evaluate the passenger-finding potential of each road cluster.（为每个road cluster 提取特征，用ELM模型给这些cluster的上车潜能排序）
2、Highlight
It is noteworthy that existing recommender systems for taxi drivers focus on recommending the passenger-finding strategy in a certain region or the most efficient path to a given destination. Different from the existing works, TaxiRec treats each road cluster as an evaluation unit and recommends the most potential road clusters to taxi drivers.
翻译过来就是：
本系统和主流的推荐系统的区别：
- 主流的系统：
推荐在某区域的寻客策略
or 推荐一条去往给定终点的有效路径 - 本系统：
把road cluster作为最小单元
直接推荐road cluster
3、Main idea
The basic idea is to treat each cluster as a sample , which is composed of n input features , and one output decision value yi (the number of pick-up actions). Then, a regression model could be constructed on labeled samples, in order to find the relationship between and . This model is then used to predict the decision value of unlabeled samples.
可以看做是 一个线性方程的回归问题，输入自变量x，得到因变量y，相对于BP算法的好处是，本算法不需要迭代。
4、总结
感觉帮助不大