Dashboard Recommendation Engine for Cross-User Behavior Analysis
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Solution Overview
Problem
Users often face frustration and inefficiency in accessing specific information from dashboards, as they are pre-defined and may not include the information users need, leading to the need for navigating multiple dashboards, which can affect task performance and productivity.
Innovation Solution
A data processing system that tracks user interactions with dashboards, performs cognitive analysis to determine user behavior patterns, and provides recommendations for relevant dashboards or modifications based on cross-user correlation analysis, using predictive analytics and cognitive computing to identify intersection points and user information needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If pre-defined dashboards are provided to users, then dashboard structure and organization are maintained, but users cannot access specific information they need and must navigate multiple dashboards
Solution Approach 1:
The system automatically tracks user interactions with dashboards and performs cognitive analysis to generate personalized recommendations without requiring users to manually search or navigate multiple dashboards. The system serves itself by learning from user behavior patterns and proactively providing relevant information access paths.
Solution Approach 2:
The system implements a feedback loop by tracking user dashboard interactions, analyzing behavior patterns through cognitive computing, and using this information to generate recommendations that are fed back to users. This closed-loop system continuously improves information accessibility based on actual user needs observed through interaction tracking.
2Loss of information
If multiple dashboards are provided to cover all information needs, then information completeness is improved, but user frustration and navigation time increase
Solution Approach 1:
The system performs preliminary analysis of user dashboard behavior patterns and pre-computes recommendation paths before users need them. By tracking interactions and analyzing patterns in advance, the system prepares personalized dashboard recommendations that reduce navigation time when users actually need information.
Solution Approach 2:
The system segments the comprehensive dashboard information into personalized recommendation subsets based on individual user behavior patterns. Instead of presenting all available dashboards, the system divides and recommends only the most relevant subset to each user, maintaining information completeness while reducing navigation burden.
3Adaptability or versatility
If dashboards are customized to include all possible information, then information availability is improved, but dashboard complexity and usability deteriorate
Solution Approach 1:
The system applies local quality by providing different dashboard recommendations to different users based on their specific behavior patterns and information needs. Instead of a uniform dashboard for all users, each user receives customized recommendations tailored to their local context and usage patterns, improving both adaptability and usability.
Solution Approach 2:
The dashboard recommendations are dynamic and adapt over time as the system continues to track user interactions and learn behavior patterns. The recommendations evolve from static pre-defined dashboards to dynamic, personalized suggestions that adjust based on changing user needs and usage patterns.
Data Source
AI summary
Mechanisms are provided for performing cross-user dashboard behavior analysis and dashboard recommendation generation. Dashboard interfaces are presented to a user and the user inputs are tracked. Cognitive analysis of the user dashboard behavior pattern data is performed to determine a reason for user dashboard behavior represented by the user dashboard behavior pattern data. Cross-user correlation analysis operations are performed based on the user dashboard behavior pattern data and dashboard behavior pattern data of other users of a different user type to identify an intersection point. A recommendation output is generated and output that recommends at least one of a particular dashboard interface to access or a modification to the one or more dashboard interfaces to be performed. The recommendation is based on the identification of the intersection point and the determined reason for the user dashboard behavior.