综合多要素的地理空间数据众包任务推荐方法

针对境外欠发达国家（或地区）地理空间数据和志愿者不足问题，为提高有限志愿者的贡献积极性和有效性，本文提出了一种综合多要素的地理空间数据众包任务推荐方法。首先采用网格将研究区域划分为若干任务；然后引入三角核函数计算用户空间偏好，结合时间遗忘率综合计算用户的时空偏好，借鉴TF-IDF和余弦相似度计算语义偏好，并融合时空、语义偏好获取初始兴趣推荐列表；最后利用隐语义模型预测用户标注每个任务的信誉（能力），并根据用户信誉对初始推荐列表重排序。为验证本文方法有效性，以有一定数据基础的巴基斯坦首都伊斯兰堡为试验区，采用OpenStreetMap平台收集的用户及众包数据开展任务区推荐试验，试验数据按照8：2的比例随机划分为训练集和测试集。试验结果表明，该方法不仅能提高推荐任务接受率，还能提高用户完成任务的有效性。

A comprehensive geospatial data crowdsourcing task recommendation method

Lack of data and volunteers in underdeveloped areas is the bottleneck that restricts global mapping task. In order to solve this problem, we propose a comprehensive geospatial data crowdsourcing task recommendation method to improve the effectiveness of limited volunteers' contributions. In this method, the research area is divided to several task areas using grids, the spatial preference is computed using triangular kernel and temporal preference is computed using exponential time forgetting rate, and TF-IDF is used to compute the semantic preferences of users. The spatio-temporal-semantics comprehensive preference is calculated using the multiplication rule. The initial task-user recommendation list can be obtained based on the spatio-temporal-semantics comprehensive preference. In order to improve the quality of the contribution data, the user reputationis introduce to our recommendation model, and the latent factor model is used to predict the user's reputation for each task area. The initial recommendation list is reordered according to the user's reputation. In order to verify the effectiveness of the method, we choose Islamabad (the capital of Pakistan) as research area because it is an underdeveloped areas with a certain data foundation. The user and crowdsourced data of Islamabad collected by the OpenStreetMap platform are used as the experiment data. The crowdsourced data is randomly divided into training and test set according to 8:2 ratio. The experimental results show that the proposed method in this paper can not only improve the acceptance rate of recommended tasks, but also can impove the quality of the contributions to some extent.