Common Features Based Volunteer and Voluntary Activity Recommendation Algorithm

In general, the dataset of volunteer recommendation systems shows the sparsity, while a volunteer recommendation system required performing the function of recommending voluntary activities interesting to a specific volunteer. To our knowledge, there exists no such kind of recommendation systems. To begin with, this paper firstly presents an analysis of a dataset collected from a real volunteering application website and discovered two features: the locations between the volunteers and the voluntary activities are in close proximity, and the resulting graph which describes the participation relationship between volunteers and voluntary activities is a kind of bipartite, showing many small communities inside it. We call the first discovery 'geographically closely participating', and the second discovery 'participating together'. Based on these findings, a rating matrix, featuring a matching method for the recommendation algorithm has been constructed. Secondly, we propose a weighted Personal Rank algorithm to implement the required functions of a volunteer recommendation system by employing the registration information of volunteers and voluntary activities. This includes the volunteers' preferences, activities and location etc. The comparison of proposed method with the rating matrix-based collaborative filter algorithm and the Personal Rank algorithms shows that our proposed method outperforms them.