# Automated Recommendation of Aggregate Visualizations for Crowdfunding Data
## 1 Introduction  
_Crowdfunding_, also called peer-to-peer lending, social lending, or crowd lending, is an internet-based fundraising mechanism soliciting small monetary contributions from crowd donors to help others in need [1, 2]. The importance of crowdfunding is further underscored as economies worldwide are racing to meet the _Sustainable Development Goals_ (SDGs) by 2030. Particularly, crowdfunding plays an important role in achieving those goals, especially the ones related to poverty, hunger, health, education, and gender equality [3]. Generally speaking, the mainstream crowdfunding platforms can be classified into four categories, i.e., donation-based, reward-based, equity-based, and lending-based ones [4]. Among them, the donation-based ones are becoming increasingly popular. Since the first crowdfunding platform Zopa was established in 2005, more lending platforms have emerged (e.g., Prosper, LendingClub, Kiva, Street Shares, Upstart, and Rernendai) [2, 4]. Among those crowdfunding platforms, _Kiva_, which is the focus of this work, is becoming an increasingly popular platform. Kiva's goal is to develop an instrument to use charitable loans to combat and eradicate poverty [5]. Lenders provide money with the understanding that they would only recoup their initial investment and lose any profit [6]. Kiva has provided people and groups with modest incomes with loans totalling more than $1.4 billion [4]. The rapid development of crowdfunding platforms, such as Kiva, has attracted much attention from the data analytics research community. Particularly, solutions have been proposed to address some of the interesting research problems that arise in crowdfundingplatforms, such as predicting project success [7], tracking the funding dynamics [8], recommending donors [9], recommending projects for donors [10; 11; 12], etc. Orthogonal to the existing work mentioned above, our focus in this paper is to utilize visual data exploration techniques, and in particular visualization recommendation systems, to unlock valuable insights from crowdfunding data, as described next. Visual data exploration is an essential step in the data science pipeline, in which analysts examine datasets up-close to extract valuable insights [13; 14; 15; 16]. This process has traditionally been performed manually, where the analyst interactively applies various exploratory queries (such as SQL-based filtering, aggregation, joins, etc.). The results of those queries are presented as data-driven visualizations (e.g., bar or line charts, scatter plots, etc.). The analyst then examines those visualizations looking for insights, which are used as a springboard to decide their next analytical query. However, unlocking those insights has been anecdotally compared to \"finding a needle in a haystack\" [17]. That is particularly true for big high-dimensional datasets with tens to hundreds of attributes and measures, as it is typically the case in financial data warehouses and scientific datasets [13]. Particularly, the \"curse of dimensionality\" leads to analysts having to manually construct a prohibitively large number of queries and visually explore their results looking for insights, which is clearly an ad hoc and labor-intensive process. That challenge motivated multiple research efforts that focused on _automatic_ recommendation for data exploration. That is, recommender systems dedicated to providing the user with suggestions for specific, high-utility visualizations (e.g., [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]). Such systems are data-driven (also known as discovery-driven) systems, which use heuristic notions of \"interestingness\" and employ them in the recommendation. The main idea underlying those solutions is to automatically generate \"all possible\" exploratory queries of the data, generate their corresponding visualizations, and recommend the _top-k_ interesting ones, where \(k\) is a user-defined parameter. Meanwhile, the interestingness of a query/visualizations is quantified using some utility metric over its result.