Personalized Visualization Recommendation via Matrix Factorization
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Solution Overview
Problem
Conventional data visualization tools lack the ability to automatically select customized visualizations for new datasets, requiring users to manually configure settings each time, and are limited by relying on explicit user feedback and failing to leverage implicit user interactions and dataset-specific preferences.
Innovation Solution
A system that uses user interaction data and meta-features to create low-dimensional embeddings, predicting visualization preferences by modeling user interactions with various datasets, allowing for personalized visualization recommendations based on both explicit and implicit feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional data visualization tools provide customizable configurations, then users can modify how data is displayed according to preferences, but users must manually select new configurations for each new dataset, increasing time consumption and operational complexity
Solution Approach 1:
The system automatically generates visualization configurations by analyzing user interaction history and dataset characteristics without requiring manual user input. The recommendation engine self-serves by predicting optimal visualizations based on learned patterns from previous interactions
Solution Approach 2:
The system collects and processes user interaction feedback (clicks, hovers, selections) to continuously improve visualization recommendations. This feedback loop enables the system to adapt to user preferences automatically, resolving the contradiction between customization and time consumption
2Measurement precision
If conventional tools rely on explicit user feedback, then visualization preferences can be captured, but implicit user interactions and dataset-specific preferences are not leveraged, reducing prediction accuracy
Solution Approach 1:
The system merges multiple data sources including explicit feedback, implicit interactions (clicks, hovers, time spent), and dataset meta-features into a unified recommendation model. This combination of previously separate information streams significantly improves prediction accuracy
Solution Approach 2:
The system transforms interaction data into low-dimensional embeddings that capture complex user preferences and dataset characteristics in a compressed feature space. This dimensional transformation enables more effective utilization of implicit interaction data
3Extent of automation
If a model uses low-dimensional embeddings of user characteristics, data attributes, and visualization configurations, then personalized recommendations can be generated automatically, but the device complexity increases due to matrix factorization and embedding computation
Solution Approach 1:
The system performs preliminary computation by pre-computing low-dimensional embeddings of users, datasets, and visualizations during idle periods or initial system setup. This advance preparation reduces real-time computational complexity when generating recommendations
Solution Approach 2:
The system transforms high-dimensional raw data into low-dimensional embedding spaces, fundamentally changing the parameter dimensions. This transformation reduces computational complexity while preserving essential information needed for accurate recommendations
Data Source
AI summary
Systems and methods for personalized visualization recommendation are described. Embodiments of the described systems and methods are configured to identify a first matrix representing user interactions with a plurality of data attributes corresponding to a plurality of datasets, a second matrix representing user interactions with a plurality of visualizations, and a third matrix representing a plurality of meta-features for each of the data attributes; compute low-dimensional embeddings representing user characteristics, the data attributes, visualization configurations, and the meta-features using joint factorization of the first matrix, the second matrix and the third matrix; generate a model for predicting visualization preference weights based on the low-dimensional embeddings; predict the visualization preference weights for a user corresponding to a plurality of candidate visualizations of dataset using the model; and generate a personalized visualization of the dataset for the user based on the predicted visualization preference weights.