Dynamic Recommender System Model Composition
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Solution Overview
Problem
Conventional recommender systems rely exclusively on a single utility model for generating recommendations, limiting their flexibility and accuracy, as they fail to adapt to dynamic user preferences and contextual information.
Innovation Solution
A comprehensive recommender system that dynamically combines multiple utility models based on contextual and preferential information, using a weight function to compute an overall rating, allowing for adaptive model selection and evolution over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single utility model is used for generating recommendations, then the system is simple to operate and implement, but the accuracy and adaptability of recommendations deteriorate
Solution Approach 1:
The patent combines multiple utility models (collaborative filtering, content-based filtering, knowledge-based models) into a unified recommendation system that integrates their outputs through weighted aggregation, thereby improving recommendation accuracy while managing system complexity through modular architecture
Solution Approach 2:
The system creates a universal recommendation framework that can accommodate multiple different utility models and adaptively select/apply them based on query context, user preferences, and item characteristics, making the system versatile across different recommendation scenarios
2Adaptability or versatility
If multiple utility models are combined dynamically, then the adaptability and accuracy of recommendations improve, but the device complexity increases
Solution Approach 1:
The system dynamically adapts its behavior by adjusting the selection and weighting of utility models based on real-time context including user preferences, query type, and item characteristics, allowing the system to optimize performance for different scenarios without manual reconfiguration
Solution Approach 2:
The recommendation system automatically selects and weights appropriate utility models based on contextual information and learned patterns from user interactions, enabling self-adaptation without requiring manual intervention to adjust system complexity or model selection
3Measurement precision
If contextual and preferential information is integrated, then the recommendation accuracy improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The system segments contextual information into distinct categories (user preferences, query context, item attributes) and processes each segment through specialized modules, making the complex task of integrating multiple information sources more manageable and efficient
Data Source
AI summary
One embodiment of the present invention provides recommender system for generating a recommendation of an item by combining a set of utility models adaptively to facilitate a decision-making process. The system includes a utility model database containing the set of utility models and a query module for receiving at least one query about the item from a querying entity. The system also includes a rule engine to specify a subset of utility models to be applied to the item and to specify a weight function of the specified utility models. Further included in the system is a set generator coupled to the utility model database, the query module, and the rule engine. The set generator computes a set of ratings by applying each of the utility model in the subset to the item and generates an overall rating for the item based on the weight function. The system further a communication module to return the overall rating.