Context-Aware Recommendation System for Analytics
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Solution Overview
Problem
Existing business intelligence analytics systems face challenges in helping users fully utilize available data due to complexity, as users often fail to explore relevant reports or tools without knowing they exist, and current virtual assistants and chatbots are limited in providing proactive recommendations without user queries.
Innovation Solution
A recommendation system trained using machine learning to predict user intent and current context, suggesting relevant analytics data or tools based on user history and interactions, allowing for personalized recommendations without requiring user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users manually explore reports in an analytics system, then they can discover relevant data, but the system complexity prevents users from fully utilizing available information
Solution Approach 1:
The system automatically generates recommendations without requiring user effort to explore and discover. The recommendation engine autonomously analyzes user context, interaction history, and report relationships to provide relevant suggestions, allowing the system to serve itself rather than requiring users to navigate complex interfaces manually
Solution Approach 2:
The recommendation system acts as an intermediary between the complex analytics system and the user. It translates the vast amount of available data and system complexity into simplified, context-relevant recommendations, bridging the gap between system capabilities and user understanding
2Loss of information
If users manually search for relevant reports, then they can find needed information, but users often fail to explore relevant reports or tools without knowing they exist
Solution Approach 1:
The system performs preliminary analysis of user context, interaction history, and report relationships before the user needs information. By pre-processing and understanding user needs through machine learning models, the system can proactively present relevant reports and tools before users search for them, eliminating the need for manual exploration
Solution Approach 2:
The system continuously monitors user interactions with reports and tools, using this feedback to refine recommendations. By analyzing what users view, how long they spend on reports, and what actions they take, the system adapts its recommendations to better match user needs, improving discovery efficiency over time
3Ease of operation
If virtual assistants process user queries, then they can provide information, but they are limited in providing proactive recommendations without user queries
Solution Approach 1:
Instead of waiting for users to query the system (traditional approach), the system inverts the interaction model by proactively pushing recommendations to users based on their context and behavior. This inversion transforms the system from a passive query-response model to an active recommendation-delivery model, enabling both ease of operation and high automation
Data Source
AI summary
Methods and systems are provided for providing recommendations from a recommendation system for an analytics system. A recommendation system can be trained using user intent and context. Such user intent can be determined using a user history of interaction with an analytics system. The user history can either be that of the user accessing the recommendation system or an exemplary user history to broaden the recommendations made by the recommendation system. Such context can be determined using context features within the analytics system. The trained recommendation system generated using user intent and context can provide analytics recommendations based on a current context of a user that predict the intent of the user.