Product Search Ranking Using Engagement Feedback Loops
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Solution Overview
Problem
Current systems lack mechanisms for selecting the right features for candidate ranking algorithms to optimize search results and product recommendations in diverse marketing spaces, leading to inefficiencies in e-commerce environments.
Innovation Solution
Implementing a Learning to Rank (LTR) algorithm, such as LambdaMART, that selects domain-specific features to optimize product recommendations by learning from user interactions through a feedback loop, using feature engineering and machine learning to refine search engine performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If generic features are used for all domains in candidate ranking algorithms, then system complexity is reduced, but search result relevance and recommendation accuracy deteriorate
Solution Approach 1:
The patent segments the monolithic candidate ranking system into multiple domain-specific ranking models, each trained on features relevant to its specific domain (e.g., electronics, fashion, books). This allows each model to use tailored features for optimal performance in its domain while avoiding the complexity of managing a single universal model with all possible features.
Solution Approach 2:
The system dynamically selects and switches between different domain-specific ranking models based on the input query and identified domain. This dynamic adaptation allows the system to use the most appropriate features and model for each specific search context, improving relevance without requiring all models to maintain all features simultaneously.
2Measurement precision
If domain-specific features are selected for each marketing space, then recommendation accuracy is improved, but device complexity increases
Solution Approach 1:
The system divides the recommendation engine into multiple specialized ranking models, each responsible for a specific domain or marketing space. Each model uses only the features relevant to its domain, reducing the complexity burden on individual models while collectively covering all domains with high accuracy.
Solution Approach 2:
The patent introduces a domain classification intermediary that identifies the appropriate domain for each query and routes it to the corresponding specialized ranking model. This intermediary layer manages the complexity of having multiple domain-specific models by providing a simple interface for query routing.
3Device complexity
If traditional search algorithms are used without machine learning, then system simplicity is maintained, but adaptability to user behavior deteriorates
Solution Approach 1:
The patent implements feedback loops where user interactions with search results and product recommendations are continuously collected and used to retrain the ranking models. This feedback mechanism enables the system to adapt to changing user behaviors and preferences over time, improving adaptability while maintaining a relatively simple overall architecture.
Solution Approach 2:
The system performs preliminary training of domain-specific ranking models using historical data before deployment. This preliminary action allows the models to learn from past user behavior in advance, so they are already adapted to user preferences when first used, reducing the need for complex real-time adaptation mechanisms.
4Quantity of substance
If generic candidate ranking models are deployed across all industries, then deployment cost is reduced, but performance optimization for specific domains deteriorates
Solution Approach 1:
The patent segments the ranking system into reusable domain-specific models that can be independently deployed and scaled. This segmentation allows organizations to deploy only the models they need for their specific industry or domain, reducing deployment costs compared to licensing or implementing a single comprehensive generic model, while maintaining high performance optimization for their specific use case.
Data Source
AI summary
Embodiments are described for monitoring a communications medium between a user and one or more other users and one or more products for selection, and collecting data about the users, the one or more products, and historical interactions among the users and the products from the monitored communications medium. Features pertaining to the collected data are defined and selected, and a candidate ranking process is trained using a LambdaMART Learning-to-Rank model based on the defined features. The model is deployed and applied to live data to produce product recommendations for display to the user. The model is retrained using refreshed data based on user behavior evidenced in the live data to update the model for the user behavior.