Trends Recommendation Model for Natural Language Data Access
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Solution Overview
Problem
Users face challenges in accessing and analyzing diverse data sources due to domain-specific languages, limiting the effectiveness of natural language interfaces in IT environments.
Innovation Solution
A method is introduced to recommend trending natural language requests by generating rating data, training a trends recommendation model using collaborative filtering machine learning algorithms, and presenting these recommendations in a query interface, allowing users to access data without explicit preference indication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language interfaces are used to enable users to access diverse data sources without DSL expertise, then ease of operation is improved, but users cannot know which NL requests can be issued, reducing productivity
Solution Approach 1:
The system performs preliminary actions by generating rating data for multiple potential NL requests and training a trends recommendation model in advance. This allows the system to proactively surface trending NL requests to users before they need to formulate their own queries, thereby improving both ease of operation and productivity by guiding users toward effective data access patterns
Solution Approach 2:
The system implements feedback mechanisms by collecting implicit user preferences through rating data generation and using this feedback to continuously train and improve the trends recommendation model. The model then provides feedback to users in the form of recommended NL requests, creating a closed-loop system that enhances both usability and productivity over time
2Measurement precision
If collaborative filtering machine learning algorithms are used to train the trends model, then measurement precision is improved for predicting user preferences, but loss of time is incurred during model training
Solution Approach 1:
The system performs preliminary action by generating rating data in advance for multiple NL requests across different data sources. This pre-generated rating data is then used to train the trends recommendation model, allowing the system to achieve high preference prediction accuracy while minimizing real-time training overhead by performing the data preparation work beforehand
Solution Approach 2:
The system applies partial action by generating rating data for a selected plurality of NL requests rather than all possible requests. This selective approach allows the collaborative filtering algorithm to achieve sufficient prediction accuracy for the most relevant queries while reducing the overall computational time and resources required for model training
Data Source
AI summary
In various embodiments, a natural language (NL) application implements functionality for recommending trending NL requests to users of the application. The functionality includes generating rating data associated with a plurality of natural language (NL) requests and one or more intents corresponding to the plurality of NL requests, wherein the rating data indicates a preference of at least one user for using at least one of the plurality of NL request to access data, training a trends recommendation model based on the rating data associated with the plurality of NL requests, generating a set of NL request recommendations based on the trends recommendation model, and causing the set of NL request recommendations to be presented in a query recommendation interface.