Dynamic Recommender System Using Adaptive Similarity Measures
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Solution Overview
Problem
Current recommender systems are inflexible and inefficient in adapting to changing user preferences and item similarities over time, as they often rely on fixed similarity measures that may not accurately reflect individual user tastes.
Innovation Solution
Implement a dynamic recommender system that allows for the use of multiple similarity measures for different users, enabling user-defined measures and the ability to switch between them based on performance, thereby providing personalized recommendations that adapt to user preferences over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed similarity measure is used for all users, then the system complexity is reduced and ease of operation is improved, but the adaptability to individual user tastes and predictive accuracy deteriorates
Solution Approach 1:
The patent segments the user base by creating distinct user profiles, where each profile can be associated with different similarity measure types (e.g., content-based, collaborative filtering, hybrid). This allows the system to apply different similarity measures to different user segments, improving adaptability while maintaining manageable system complexity through organized user segmentation.
Solution Approach 2:
The system dynamically selects and switches between different similarity measure types based on user profile characteristics, item characteristics, and contextual factors. This dynamic adaptation allows the system to optimize recommendation quality for each user without requiring manual configuration, resolving the contradiction between ease of operation and adaptability.
2Adaptability or versatility
If multiple similarity measures are maintained and switched between for different users, then adaptability to user preferences is improved, but the device complexity and computational requirements increase
Solution Approach 1:
The patent implements local quality by associating specific similarity measure types with specific user profiles or item categories rather than applying a single measure globally. Each user profile can have customized similarity measure preferences based on their behavior patterns, allowing the system to maintain multiple measures only where needed, thus improving adaptability without proportionally increasing overall system complexity.
Solution Approach 2:
The system changes parameters dynamically by selecting different similarity measure types (e.g., switching between cosine similarity, Pearson correlation, or custom metrics) based on user profile attributes and contextual conditions. This parameter-based approach allows flexible adaptation while managing complexity through conditional selection rather than maintaining all measures simultaneously for all users.
3Measurement precision
If similarity measures are updated frequently to adapt to changing user tastes, then predictive accuracy is improved, but the loss of time and computational resources increases
Solution Approach 1:
The system employs periodic updates of user profiles and similarity measure selections rather than continuous real-time updates. User behavior is monitored and accumulated over time, with profile updates and similarity measure reselection occurring at scheduled intervals or when threshold conditions are met. This periodic approach maintains predictive accuracy by capturing changing user tastes while reducing computational overhead compared to continuous updates.
Solution Approach 2:
The system performs preliminary analysis and user profile creation in advance, pre-computing user preferences and associating appropriate similarity measure types before actual recommendation generation. This preliminary action allows the system to quickly retrieve and apply pre-determined similarity measures during recommendation operations, improving predictive accuracy while minimizing real-time computational time and resource consumption.
Data Source
AI summary
Various techniques can be used to implement a collaborative filtering-based recommendation engine. For example, different similarity measures can be used for different users. Different similarity measures can be used for a particular user across time. A superior similarity measure can be found for a user. User-defined similarity measures can be supported.