Search Result Summarization for Product Similarity and Difference Comparison
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Solution Overview
Problem
Existing search engines fail to effectively present and compare product features, leading to a cognitively loaded search process that hinders users from understanding and assessing search results efficiently.
Innovation Solution
A summarization system utilizing a transformer encoder-decoder architecture and large language models to analyze user queries, generate item summaries, and prompt users with ideation questions to refine their search based on user preferences and intentions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional search engines present detailed product descriptions and features, then users can access comprehensive information, but the search process becomes cognitively loaded and difficult to assess results efficiently
Solution Approach 1:
The system extracts key product features and attributes from detailed descriptions using NLP techniques, separating essential information from verbose text. This allows comprehensive feature data to be captured while presenting only the most relevant extracted features to users in a condensed format, reducing cognitive load while maintaining information completeness.
Solution Approach 2:
The system applies different levels of information processing to different parts of product data. Detailed descriptions are fully analyzed and structured in the backend, while the frontend presentation provides summarized key features. This local differentiation allows comprehensive information retention with efficient user-facing display.
2Measurement precision
If search engines provide comprehensive product comparisons, then users can make informed decisions, but the complexity of presenting and comparing multiple product features increases
Solution Approach 1:
The system replaces manual feature extraction and comparison mechanisms with automated NLP-based processing. Machine learning models automatically parse product descriptions, extract features, and structure comparison data, eliminating the need for complex manual curation systems while maintaining high comparison accuracy.
Solution Approach 2:
The system transforms unstructured product descriptions into structured feature parameters that can be easily compared. By converting text into standardized parameter formats (e.g., extracting specific attributes like battery life, processor speed, dimensions), the system enables precise comparisons without requiring complex presentation structures.
3Loss of information
If users manually analyze detailed product descriptions, then they can understand product features, but the time required to assess search results increases
Solution Approach 1:
The system performs preliminary analysis of product descriptions before user interaction. NLP models pre-extract key features, generate summaries, and structure comparison data in advance, so that when users view search results, the information is already processed and ready for quick assessment, eliminating the need for users to manually analyze detailed descriptions.
Solution Approach 2:
The system creates condensed copies of detailed product descriptions that capture essential features in a simplified format. These summaries preserve the core information needed for product understanding while being significantly more concise than full descriptions, enabling rapid user assessment.
Data Source
AI summary
One or more aspects of the method, apparatus, and non-transitory computer readable medium include receiving a query relating to an item and a summarization type indicating an emphasis on item similarities or item differences, obtaining, using a search component, descriptions of items relevant to the query, generating input data for a machine learning model based on the descriptions and the summarization type, and generating, using the machine learning model, a summarization of the descriptions based on the input data in response to the query, wherein the summarization emphasizes the item similarities or item differences based on the summarization type.