Verbal Query Recommendation System Using Modular Ranking
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Solution Overview
Problem
Existing technologies for generating responses to verbal queries are limited by restricted data sources and inflexible ranking models, leading to decreased response quality as the complexity of queries increases.
Innovation Solution
A computing system that transforms verbal queries into searchable queries using automated speech recognition, reformulates them, and generates query signals to produce more reliable rankings by utilizing multiple data repositories and customizable filtering and blending functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple data sources and customizable ranking models are used, then response quality and relevance are improved, but system complexity increases
Solution Approach 1:
The system segments the ranking process into multiple independent ranking models (e.g., relevance ranking, diversity ranking, popularity ranking) that can be applied separately to different aspects of candidate responses. Each ranking model operates on specific features and can be independently configured, allowing high response quality through multiple specialized ranking functions while managing complexity through modular design.
Solution Approach 2:
The system implements a universal ranking framework that can accommodate multiple different ranking models and data sources through a common architecture. The blending function serves as a universal mechanism that integrates results from various ranking models and data sources (e.g., user profile data, product catalog data, historical interaction data) into a unified ranking, allowing the system to handle diverse requirements without proportionally increasing complexity.
2Adaptability or versatility
If flexible and personalized ranking models are implemented, then adaptability to different queries is improved, but computational resources required increase
Solution Approach 1:
The system dynamically selects and configures which ranking models to apply based on the characteristics of each verbal query. The blending function parameters can be adjusted in real-time based on query complexity, user preferences, and contextual information. This dynamic adaptation allows the system to use more computational resources only when necessary for complex queries while maintaining adaptability across different query types.
Solution Approach 2:
The system changes parameters of the ranking models based on the specific query being processed. The blending function uses weighted combinations of different ranking signals, and these weights can be dynamically adjusted based on query features, user profiles, and contextual factors. This parameter adjustment allows flexible and personalized ranking without always requiring full computational resources, as simpler queries can use simplified parameter configurations.
3Measurement precision
If automated speech recognition and query reformulation are used, then accuracy of query understanding is improved, but processing time increases
Solution Approach 1:
The system performs preliminary processing of verbal queries through automated speech recognition and basic reformulation before the main ranking process. Query signals are extracted and preliminary candidate responses are identified in advance. This preliminary action improves query understanding accuracy by ensuring proper interpretation before ranking, while the pre-processing nature helps manage overall processing time by preparing data structures ahead of time.
Solution Approach 2:
The system replaces manual query interpretation with automated speech recognition and machine learning-based reformulation models. This substitution of mechanical/manual processes with automated computational processes improves accuracy of query understanding while potentially reducing processing time through efficient algorithmic processing compared to human analysis.
Data Source
AI summary
Technologies are provided for generation of recommendation results using a verbal query. In one embodiment, query data can be generated using a searchable query corresponding to the verbal query. The query data can define a query browse node and a product brand, for example. First product identifiers that match the searchable query can be determined using multiple data repositories. Duplicates from the first product identifiers can be removed, resulting in second product identifiers. Attribute data also can be generated using the second product identifiers. The attribute data can define features for a product identifier. Further, third product identifiers can be determined by applying a filtering model to the second product identifiers. A ranking of the third product identifiers can be generated using an optimization function based on the query data and the product attribute data. A product corresponding to one of the ranked product identifiers represents a recommendation result.