Conversation-Based Product Search With Contextual Query Enhancement
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Solution Overview
Problem
Existing product search systems fail to provide accurate and personalized recommendations due to a lack of consideration for the context behind customer queries, requiring customers to perform extensive research and often leading to incorrect product selections.
Innovation Solution
A system utilizing natural language models and machine learning to analyze customer conversations, generate enhanced queries, and recommend products based on contextual information, ensuring personalized and accurate product suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search engines are used to return search results matching queries, then the search process is simple and fast, but the accuracy and relevance of product suggestions deteriorate because contextual information is not considered
Solution Approach 1:
The system performs preliminary actions by extracting contextual information from conversation history before the actual search occurs. The natural language model analyzes previous turns of conversation to understand customer intent and preferences, preparing enriched query representations that incorporate contextual nuances before the search query is executed, thereby improving suggestion accuracy without adding complexity during the search execution phase
Solution Approach 2:
A natural language model serves as an intermediary component between the customer's simple search query and the search engine. This intermediary extracts and processes contextual information from conversation history, transforming the original query into an enhanced representation that captures customer intent and preferences, thereby bridging the gap between simple query input and accurate product recommendations
2Measurement precision
If customers perform heavy research to know what products to search for, then they can find more relevant products, but the time required for product discovery deteriorates
Solution Approach 1:
The system performs self-service by automatically extracting and processing contextual information from the conversation history without requiring customer intervention. The natural language model independently analyzes previous conversation turns, identifies customer preferences and intent, and uses this information to enhance search queries, thereby providing relevant product suggestions without requiring customers to perform heavy research or manual information gathering
Solution Approach 2:
The system incorporates feedback from conversation history to continuously refine and enhance search queries. By analyzing previous customer inputs and interactions, the natural language model adjusts and optimizes the search query representations, creating a feedback loop that improves the relevance of product suggestions over time without requiring additional customer effort or time investment
3Adaptability or versatility
If the product pool size is dramatically increased to meet customer needs, then more product options are available, but the difficulty of detecting and measuring customer preferences deteriorates
Solution Approach 1:
The system addresses the dimensionality challenge by introducing a new dimension of analysis - contextual information from conversation history. Instead of relying solely on explicit search queries, the natural language model analyzes the semantic meaning and intent behind customer conversations, adding a layer of contextual understanding that enables effective preference detection even within dramatically enlarged product pools
Data Source
AI summary
Systems and methods for performing product search based on conversations with customers are disclosed. In some embodiments, a disclosed method includes: receiving, from a computing device, a search request identifying a query and contextual information; determining, using a natural language model, at least one query entity based on the query and the contextual information; generating at least one enhanced query based on the at least one query entity and an enhancement phrase; searching a database to identify a set of items using at least one machine learning model based on the at least one enhanced query; generating a ranked list of items based on the set of items; and transmitting, to the computing device, the ranked list of items in response to the search request.