Brands Engine for Cross-Category Recommendation
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Solution Overview
Problem
Existing database management systems fail to effectively provide personalized brand-based recommendations to users in online marketplaces, as they lack the ability to accurately identify and utilize brand relationships across different product categories and user queries.
Innovation Solution
A brands engine is implemented within a client-server system that analyzes user queries to identify brand relationships by expanding seed sets of brands through pattern mining and semantic analysis, mapping brands to categories, and generating recommendations based on user behavior and query patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing database management systems are used, then basic query functionality is provided, but personalized brand-based recommendations cannot be generated
Solution Approach 1:
The patent implements a nested database structure where a brands database is embedded within the existing online publication system database. The brands database contains nested tables (brand-table, category-table, item-table) that store hierarchical relationships between brands, categories, and items. This nesting allows the system to provide brand-based recommendations while maintaining integration with the existing database management infrastructure.
Solution Approach 2:
The patent introduces a brands engine as an intermediary component between the user query interface and the existing database system. This engine receives user queries, processes them through the nested brands database using SQL queries, and generates personalized brand recommendations. The brands engine acts as a mediator that translates general queries into brand-specific recommendations without requiring complete system redesign.
2Measurement precision
If brand relationships are analyzed across multiple categories, then recommendation accuracy improves, but query processing time increases
Solution Approach 1:
The patent pre-establishes the nested database structure with brand-category-item relationships before queries are submitted. The brand-table, category-table, and item-table are populated in advance with hierarchical mappings, allowing the brands engine to quickly retrieve pre-computed brand relationships across categories without performing complex analysis during query processing. This preliminary structuring enables fast retrieval of cross-category brand recommendations.
Solution Approach 2:
The patent segments the recommendation problem into distinct database tables and query components. The brand-table stores brand information, the category-table stores category information, and the item-table stores item-brand-category relationships. By segmenting data storage and query processing into separate manageable components, the system can efficiently handle cross-category brand analysis without processing the entire database at once, reducing query processing time while maintaining recommendation accuracy.
Data Source
AI summary
A method and a system for providing recommendations based on branding are disclosed. In example embodiments, an index comprising predetermined brand relationships is maintained. Each predetermined brand relationship comprises a first brand, a second brand, and a recommendation score between the first brand and the second brand. A corpus containing a plurality of user queries is also maintained. A seed set of brands corresponding to a category in the index is expanded by accessing the corpus containing the plurality of user queries, evaluating user queries of the plurality of user queries that contain a disjunction of brand terms, and identifying a new brand to add to the seed set based on the evaluating.