Product Type Ambiguity Scoring for Implicit Intent Detection
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Solution Overview
Problem
Existing systems struggle to accurately identify and understand implicit user intents in queries, particularly those related to product type specificity, leading to less relevant search results.
Innovation Solution
A system utilizing a processor and non-transitory computer-readable media to generate ambiguity scores for queries, propagate these scores, and train a machine-learning classifier to enhance the understanding of user intents based on product type specificity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If explicit intents are used for product types, then query understanding is improved, but implicit intents become harder to understand
Solution Approach 1:
The system pre-calculates and stores ambiguity scores for product types based on historical purchase data before queries are processed. This preliminary action enables the system to quickly retrieve and apply ambiguity scores to understand implicit intents without adding real-time processing complexity.
Solution Approach 2:
The patent introduces an intermediary mechanism (ambiguity score propagation) that bridges explicit product type information and implicit user intents. The ambiguity scores act as a mediator that translates explicit product data into insights about implicit user preferences and intentions.
2Measurement precision
If ambiguity scores are generated for all queries, then query ambiguity identification is improved, but system complexity increases
Solution Approach 1:
The system segments the ambiguity scoring process into distinct components: product type ambiguity scores, query ambiguity scores, and propagated ambiguity scores. This segmentation allows each component to be calculated and optimized independently, reducing overall system complexity.
Solution Approach 2:
Product type ambiguity scores are pre-calculated and stored in a database before being needed for query processing. This preliminary action eliminates the need to compute these scores in real-time, significantly reducing system complexity during query handling.
3Reliability
If machine learning classifiers are trained on query data, then implicit intent understanding is improved, but training time and computational resources increase
Solution Approach 1:
The system uses propagated ambiguity scores from a subset of queries to train the machine learning classifier, rather than requiring comprehensive training data from all possible queries. This partial action approach achieves sufficient model accuracy while reducing training time and computational resources.
Solution Approach 2:
The system prepares training data by pre-propagating ambiguity scores through the query database before initiating machine learning training. This preliminary data preparation ensures that the classifier receives high-quality training data, improving training efficiency and reducing the time needed to achieve reliable implicit intent understanding.
Data Source
AI summary
A system including a processor and a non-transitory computer-readable media storing computing instructions that, when executed on the processor, cause the processor to perform certain operations: generating a first ambiguity score for a first query; propagating the first ambiguity score for the first query to generate a second ambiguity score for a second query; training a machine-learning classifier at least based on the first query and the second query; and generating, using the machine-learning classifier, a third ambiguity score for a third query. Other embodiments are described.