Context-Aware Data Recommendation Engine for Automated Dataset Discovery
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Solution Overview
Problem
Users face a burdensome and inefficient process when searching for relevant data resources while working with datasets, as they must manually know what to search for and lack personalized search recommendations based on user context.
Innovation Solution
A method that derives source attributes from user and data information to identify and recommend target data and services that have been found useful to other users working on similar datasets, using a combination of statistical and rule-based algorithms to provide automated and context-aware recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually search for data resources using search tools, then they can find relevant data, but the process becomes burdensome and time-consuming
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing user profiles, data resource metadata, and similarity models before users need to search for data. When a user accesses a dataset, the system has already prepared recommendation candidates based on historical user behavior patterns and data relationships, eliminating the need for manual search operations.
Solution Approach 2:
The system enables self-service by automatically generating personalized data recommendations based on user context without requiring user input or intervention. The recommendation engine autonomously analyzes user profiles, accessed datasets, and data resource metadata to present relevant data sources, allowing the system to serve itself rather than requiring active user search.
2Ease of operation
If users use search tools to find data, then they can locate relevant information, but they must know what to search for and provide relevant input
Solution Approach 1:
The system introduces an intermediary layer in the form of an intelligent recommendation engine that mediates between the user's data access needs and the available data resources. This intermediary automatically translates user context into relevant data recommendations without requiring users to formulate search queries or provide explicit input, simplifying the operation while maintaining relevance.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user interactions with data resources and adjusting recommendations accordingly. User feedback signals (such as viewing, downloading, or accessing data) are fed back into the recommendation model to refine future suggestions, creating a adaptive system that improves ease of operation over time without increasing complexity.
3Adaptability or versatility
If search tools are used without user context information, then searches can be performed, but relevant data recommendations cannot be personalized
Solution Approach 1:
The system performs preliminary action by pre-building comprehensive user profiles that capture contextual information about user behavior, preferences, and data interaction patterns before actual data access occurs. These pre-computed profiles preserve and utilize user context information to enable highly personalized recommendations, preventing loss of valuable user-specific data.
Solution Approach 2:
The system achieves universality by creating a multi-functional recommendation framework that handles diverse user contexts and data types through a unified approach. The same recommendation engine adapts to different users, datasets, and scenarios by leveraging shared user profiles and data relationships, enabling personalization without requiring separate systems for each context.
Data Source
AI summary
The present invention extends to methods, systems, and computer program products for recommending data based on user and data attributes. User information and accessed data sets are periodically (and possibly automatically) accessed and updated. Source attributes are derived from user information and accessed data sets. Target attributes are derived from data directories and data services. Source attributes for an accessed data set are used along target attributes for a data directory or data service to determine the desirability of data directory or data service as a source of data relevant to the accessed data set. The data directory and/or data service can be recommended as able to provide relevant data. Accordingly, recommend relevant data can be recommended to a user without the user having to expressly search for the relevant data or even know that the relevant data exists.