Recommendation Engine Model Segmentation for Fast Personalized Recommendations
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Solution Overview
Problem
Recommender systems face challenges in efficiently recommending items to users by providing too many options or not considering user preferences accurately, leading to overwhelming choices and suboptimal recommendations.
Innovation Solution
A process utilizing a set of primary and secondary models, where each item is associated with one primary and one secondary model, calculates a confidence level and generates parameters based on user inputs, allowing for parallel processing and quick response times, and includes training with real-world data to refine recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional recommender systems provide many item options to users, then users have more choices, but users become overwhelmed and experience suboptimal recommendations
Solution Approach 1:
The patent segments the recommendation task into two distinct models: a primary model that calculates confidence levels for item recommendations, and a secondary model that generates parameters for recommended items. This segmentation allows the system to handle recommendation accuracy and parameter generation separately, reducing overall system complexity while maintaining adaptability.
Solution Approach 2:
The patent introduces confidence levels as an intermediary metric between user inputs and item recommendations. The primary model calculates confidence levels based on user preferences, and only items exceeding a threshold confidence level are recommended. This intermediary mechanism filters out suboptimal recommendations and prevents user overwhelm.
2Productivity
If the system processes recommendations sequentially, then model accuracy can be maintained, but response time increases and efficiency decreases
Solution Approach 1:
By segmenting the recommendation system into independent primary and secondary models, the patent enables parallel processing of confidence level calculation and parameter generation. This segmentation maintains model accuracy while significantly improving response speed through concurrent execution of multiple models.
Solution Approach 2:
The primary model performs preliminary action by calculating confidence levels for all items before the secondary model generates parameters. This preliminary filtering ensures that only high-confidence recommendations proceed to parameter generation, maintaining recommendation quality while reducing the computational burden on subsequent processing stages.
3Adaptability or versatility
If the system uses a single model for multiple items, then device complexity is reduced, but the ability to provide accurate personalized recommendations decreases
Solution Approach 1:
The patent applies segmentation by creating one primary model and one secondary model for each item in the catalog. This item-specific modeling approach enables highly accurate personalized recommendations by capturing unique characteristics of each item, while the modular structure keeps device complexity manageable through standardized model architectures.
Solution Approach 2:
Each primary and secondary model is tailored to its specific item, providing local quality optimization. The models capture item-specific patterns and user preferences related to that particular item, enabling precise personalization without requiring a single complex universal model.
Data Source
AI summary
A process for recommending items comprises receiving a first input from a user via a user interface and modifying the user interface based on the first input. A second input is received from the user via the user interface. The first and second inputs are used as data for running a set of primary models and a set of secondary models, where each item that the process may recommend corresponds to one and only one primary model and one and only one secondary model. The primary models calculate a confidence level that the user should be recommended the item associated with the primary model, based on the first and second inputs. Items associated with primary models where the confidence level is above a threshold are recommended to the user. For each recommended item, a parameter is generated by the associated secondary model.