# Automated Recommendation of Aggregate Visualizations for Crowdfunding Data
## 2 Materials and Methods
### The LoanVis Visualization Recommendation System
#### 2.2.2 Aspect-Based Recommendation  
Notice that in the previous discussion, the analyst had to specify two inputs in their exploratory query: (1) an _aspect_ for analysis (e.g., sector), and (2) a specific _value_ within that aspect (e.g., entertainment). Such specification is realized using the exploratory query predicate \(T\) (e.g., T: WHERE sector = Entertainment). However, working with the Kiva dataset, we have learned firsthand that it is often challenging to specify those aspects and their corresponding values, which might eventually lead to some interesting visualizations being recommended!For instance, an analyst might assume that an exploration based on WHERE sector = education or WHERE sector = agriculture would lead to some interesting recommended visualizations. However, during our analysis, we realized that all the possible visualizations, which are based on those two particular sectors, exhibit very low deviation, including the top-k ones. That is, there was nothing unique about the loans disbursed to those sectors, and their patterns followed the same pattern as that of the aggregated loans disbursed to all sectors. It was only after several rounds of experimenting that we discovered that the visualizations based on the Entertainment sector are the ones that reveal some interesting insights. Same for the visualizations based on the Wholesale and Construction sectors, which are presented in the next section. However, current visualization recommendation systems (e.g., [18; 26; 27]) assume that the analyst is able to formulate a well-defined query that selects a subset of data, which leads to insightful visualizations being recommended (i.e., visualizations with a high utility score). That is, they are limited to only recommending interesting visualizations based on a precise exploratory query for which the analyst provides all the necessary query filters. Meanwhile, in reality, it is typically a challenging task to pose an exploratory query, which can immediately reveal some insights. Hence, it is a continuous process of trial and error, in which the analyst keeps refining their query filters manually and iteratively until some interesting visualizations are recommended. Therefore, in our design of LoanVis, we emphasize that, in addition to the existing techniques for automatically recommending interesting views, there is an equal need for additional techniques that can also automatically select subsets of data that would potentially provide such interesting views. Hence, our goal in this work is not only to recommend interesting visualizations but also to recommend exploratory queries that lead to such visualizations. To achieve that goal, LoanVis expands and explores a larger search space of possible visualizations in order to recommend the top-k most insightful ones. In particular, we introduce the _aspect-based_ recommendation, where an aspect could be any of the dimension or measure attributes of the analyzed dataset. More formally, in addition to the set of dimensions \(\mathbb{A}\) and measures \(\mathbb{M}\), we introduce the set of aspects \(\mathbb{C}\), where \(\mathbb{C}=\mathbb{A}\cup\mathbb{M}\). Hence, for a given aspect \(C\in\mathbb{C}\), LoanVis explores its distinct values searching for those that might result in high-utility visualizations. Particularly, for an aspect \(C\) (e.g.,  
Figure 3: Deviation in loans distribution for all sectors (comparison view in black) vs. loans for the entertainment sector (target view in red). sector_), which takes a set of distinct values: \(c_{1},c_{2},\ldots\), LoanVis iterates through all the distinct values in \(C\) (Algorithm 1 line 1). Then, for each distinct value \(c_{i}\), it generates all the possible visualizations that are based on selecting the subset of data that satisfies that value \(c_{i}\) (e.g., Entertainment) (Algorithm 1 lines 4-6). That process is repeated for all values in \(C\), and the top-k visualizations with the highest deviation values are recommended to the analyst (Algorithm 1 line 10).