Search Ranking Using Vector Similarity for Regional Queries
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Solution Overview
Problem
Current online marketplace systems face challenges in accurately ranking listings based on varying query features such as geographic region and dates, often presenting less relevant listings higher due to the use of single algorithms that do not consider regional and temporal variations in pricing and user preferences.
Innovation Solution
The implementation of a combination of query feature machine learning models and listing feature machine learning models to generate target and listing vectors, allowing for the determination of similarity between listings and search queries, thereby ranking listings based on their relevance to the user's specific needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single algorithm is used to rank listings, then the system complexity is low, but the ranking accuracy and relevance to user preferences deteriorates
Solution Approach 1:
The patent segments the ranking system into multiple independent algorithms, each specialized for different query features such as geographic region, dates, and user preferences. This segmentation allows each algorithm to optimize for specific conditions while maintaining overall system manageability through modular architecture.
Solution Approach 2:
The system dynamically selects and applies different ranking algorithms based on the specific query features and conditions. Rather than using a static single algorithm, the system adapts its ranking approach in real-time based on the input parameters, improving accuracy without requiring all algorithms to run simultaneously.
2Adaptability or versatility
If multiple machine learning models are used to account for regional and temporal variations, then the ranking relevance improves, but the computational complexity increases
Solution Approach 1:
The patent applies local quality by creating specialized machine learning models tailored to specific geographic regions and time periods. Each model is trained on local data patterns and preferences, allowing the system to adapt to regional variations in pricing and user behavior without requiring a complete redesign of the overall ranking system.
Solution Approach 2:
The system changes parameters such as geographic region, dates, and user preference weights dynamically based on the query input. This allows the same underlying machine learning framework to adapt to different conditions by adjusting its parameters rather than requiring entirely separate systems for each scenario.
3Adaptability or versatility
If traditional ranking methods are used, then the processing speed is fast, but the ability to account for varying user preferences and market conditions deteriorates
Solution Approach 1:
The patent implements preliminary action by pre-training multiple machine learning models on historical data and storing pre-computed ranking parameters. When a search query is received, the system can quickly apply these pre-trained models and pre-computed parameters rather than performing complex calculations in real-time, thus maintaining fast processing speed while incorporating sophisticated adaptive ranking capabilities.
Data Source
AI summary
Systems and methods are provided for search result optimization using machine learning models. A search system uses machine learning models generate a target vector based on query features of a search query and a set of listing vectors based on listing features of listings identified as part of the search query. The target vector represents an estimated optimal listing for the search query and each listing vector represents a corresponding listing identified as part of the search query. The search system determines distances (e.g., Euclidian distance) between each listing vector and the target vector. The determined distances indicate how similar each listing is to the estimated optimal listing for the search query. The search system ranks the listings based on the distances such that listings that are similar to the estimated optimal listing are ranked higher than listing that are not similar to the estimated optimal listing.