这篇文章主要做的一件事（解决的问题）： 出租车空车游走路径推荐：使之在有限的行程内，尽可能在多种约束下，载客的可能性最高（或者定义的某种评估值最大）
在没有推荐系统的情况下：出租车一般会随机或者根据自己的经验选择游走的路径，以便空车率尽可能的低；路径推荐系统的目的就是去经验，科学的分配出租车游走分布。
本文的解决思路：
（1）首先根据起点位置和对应的路径组合（在满足一定的约束下）确定出租车候选路径集合；
（2）作者通过自己对于路径选择的理解，制定了计算每条路径价值的评估函数EDC（如：每条路段的driving cost; 每条路段的success probability；以及每条路段的sharing capacity）
(3) 根据EDC，就可以为每个出租车选择路径（针对每个出租车而言，当前的推荐不一定是最优的路径）；出于推荐的公平性原则，本文利用EDC又构造了每个出租车的balance，通过balance和当前所以候选路径的EDC，确定每个出租车的最终推荐路径。其中balance的存在，主要使得每一次的推荐能使得所有出租车的balance的偏差最小，以达到公平推荐的目的。
本文值得借鉴的地方：
（1）提出问题和解决问题的思路（候选集合，评估排序，核心点在于二次排序和选择的道理）
（2）EDC的构造方法值得借鉴（路段cost，路段接客成功率，路段接客成功率随路径长短的变化，路段sharing capacity对EDC的影响）
（3）出租车是被推荐的受益者，路径是推荐的实体（实体有评估好坏的标准），实体有资源的限制，被推荐受益者直接又有竞争冲突（此思路是否可以用在其他推荐系统中：候选列表的再排列 ref: http://nuoku.vip/users/2/articles/51 ）
（4）Simulation experiment 借鉴
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| SCRAM: A Sharing Considered Route Assignment Mechanism for Fair Taxi Route Recommendations | |||||||||||
| 文献作者 | Shiyou Qian; Jian Cao | ||||||||||
| 文献发表年限 | 2015 | ||||||||||
| 文献关键字 | |||||||||||
| Recommender Systems; Assignment Mechanism; Fairness; Taxis | |||||||||||
| 摘要描述 | |||||||||||
| Recommending routes for a group of competing taxi drivers is almost untouched in most route recommender systems. For this kind of problem, recommendation fairness and driving efficiency are two fundamental aspects. In the paper, we propose SCRAM, a sharing considered route assignment mechanism for fair taxi route recommendations. SCRAM aims to provide recommendation fairness for a group of competing taxi drivers, without sacrificing driving efficiency. By designing a concise route assignment mechanism, SCRAM achieves better recommendation fairness for competing taxis. By considering the sharing of road sections to avoid unnecessary competition, SCRAM is more efficient in terms of driving cost per customer (DCC). We test SCRAM based on a large number of historical taxi trajectories and validate the recommendation fairness and driving efficiency of SCRAM with extensive evaluations. Experimental results show that SCRAM achieves better recommendation fairness and higher driving effi- ciency than three compared approaches. | |||||||||||
