# Automated Recommendation of Aggregate Visualizations for Crowdfunding Data
## 2 Materials and Methods
### The LoanVis Visualization Recommendation System
#### 2.2.1 Recommending Insightful Visualizations  
Typically, a data analyst is keen to find visualizations that reveal some interesting insights about the analyzed data. For instance, to conduct studies similar to [2; 30; 33; 29], an analyst would be exploring the Kiva dataset looking for visualizations that might reveal interesting discrepancies or anomalies in loan disbursement and distribution. That is, analysts need 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. Particularly, the complexity of the manual visual data exploration process is contributed to: (1) the large number of possible visualizations, and (2) the uncertainty about the interestingness of each visualization. The challenges mentioned above motivated multiple research efforts that focused on _automatic recommendation_ for visual data exploration. That is,  
Figure 2: Target View (Entertainment sector) vs. Comparison View (All sectors). recommender systems that provide analysts with suggestions of interesting visualizations based on some objective, well-defined quantitative metrics (e.g., [21; 24; 26; 27]). For example, DeepEye is a visual insight recommendation system that employs a supervised machine learning approach to capture human perception by understanding existing examples [20]. QuickInsights [27] supports multiple types of data-driven insights for a comprehensive analysis (e.g., correlation, skewness in data distribution, diversity, etc. ), and our work [37] studies the impact of data quality problems on discovering those insights. Meanwhile, SeeDB is one of the first visual insight recommendation systems that recommends top-k aggregate visualizations based on data-driven, deviation-based approach [19; 21]. Other works that leverage a deviation-based approach include MuVE [23], which addresses binning problems in visualization recommendation systems. For further details, we refer the reader to a comprehensive recent survey on this topic [16]. In this work, and similar to several existing approaches (e.g., [21; 23; 27]), we adopt a _deviation-based_ metric, which is able to provide analysts with interesting visualizations that highlight some of the particular patterns of the analyzed datasets. In particular, the deviation-based metric measures the distance between \(V_{i}(R)\) and \(V_{i}(D)\). That is, it measures the deviation between the aggregate view \(V_{i}\) generated from the subset data \(R\) vs. that generated from the entire database \(D\). As such, \(V_{i}(R)\) is denoted as _target_ view (e.g., \(\texttt{sector}=\texttt{Entertainment}\)), whereas \(V_{i}(D)\) is denoted as _comparison_ view (e.g., \(\texttt{sector}=\texttt{ALL}\)). The premise underlying the deviation-based metric is that a view \(V_{i}\) that results in a higher deviation is expected to reveal some interesting insights that are very specific to the subset \(R\) and distinguish it from the general patterns in \(D\). That is particularly important when exploring the Kiva dataset since the deviation-based metric is naturally able to capture and quantify anomalies and discrepancies in loan distribution, which has been one of the main focuses of existing work (e.g., [2; 29; 30; 33]). To ensure that all views are of the same scale, each target view \(V_{i}(R)\) is normalized into a _probability distribution_\(P[V_{i}(R)]\), and similarly, each comparison view into \(P[V_{i}(D)]\). Particularly, consider an aggregate view \(V=(A,M,F)\). A bar chart visualization of that aggregate view can be represented as the sequence of pairs: \(<(a_{1},f_{1})\), \((a_{2},f_{2})\), \(\ldots\), \((a_{l},f_{l})>\), where \(l\) is the number of distinct values (i.e., groups) in the dimension attribute \(A\), \(a_{i}\) is the \(i\)-th group in attribute \(A\), and \(f_{i}\) is the aggregated value \(F(M)\) for the group \(a_{i}\).