Preference Learning with Clustering for New User Recommendations
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Solution Overview
Problem
Conventional recommendation systems perform poorly when encountering new users, as they rely heavily on existing data and fail to explore potential user preferences, leading to stagnated performance over time.
Innovation Solution
A method that determines whether to exploit existing data or explore new preferences by selecting user clusters based on ranking, using strategies like ε-greedy and proximity weights, and assigning preference weights based on user interactions, allowing for personalized recommendations even without prior data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional recommendation systems rely primarily on a priori knowledge of individual user preferences, then recommendations can be generated for users with existing data, but performance stagnates and cannot effectively handle new users without existing data
Solution Approach 1:
The patent segments users into multiple user clusters based on shared characteristics and preferences. Each user cluster represents a group of users with similar behavior patterns, allowing the system to handle new users by assigning them to appropriate clusters even without individual historical data. This segmentation resolves the contradiction by enabling recommendations for new users through cluster-based inference while maintaining personalized recommendations for returning users.
Solution Approach 2:
The patent introduces user clusters as an intermediary layer between individual users and the recommendation system. For new users without direct interaction history, the system uses cluster-level data as an intermediary to generate recommendations. This intermediary mechanism allows the system to bridge the gap between having no user-specific data and providing personalized recommendations, thereby improving adaptability to new users while maintaining recommendation reliability.
2Productivity
If recommendation systems single-mindedly exploit existing user preference data, then recommendations can be generated efficiently, but opportunities to discover new user preferences are foregone
Solution Approach 1:
The patent implements a dynamic recommendation approach where the system can switch between exploiting existing user preferences and exploring new preferences based on contextual factors. The system dynamically adjusts between providing recommendations based on established user profiles and introducing novel items that may reveal new user interests. This dynamic behavior resolves the contradiction by balancing efficiency (through exploitation) with preference discovery (through exploration) at different times and for different users.
Solution Approach 2:
The patent changes key parameters such as the exploration-exploitation balance, confidence thresholds, and recommendation diversity controls to optimize between efficiency and preference discovery. By adjusting these parameters dynamically based on user characteristics, interaction history, and system state, the system can shift between exploiting known preferences (for efficiency) and exploring new preferences (for discovery), thereby resolving the contradiction between productivity and information loss.
Data Source
AI summary
In one aspect, there is provided a method for preference learning. The method may include: determining whether existing data associated with a user is available, wherein the existing data includes preference weights assigned to user clusters; when existing data associated with the user is unavailable: selecting, based on a ranking of the user clusters, a user cluster; providing, based on a selected user cluster, recommendations to the user; and assigning, based on a response of the user to the recommendations, a preference weight to each of the selected user cluster and/or another user cluster; and when existing data associated with the user is available, determining whether to exploit the existing data or to explore new preferences for the user, wherein exploiting comprises providing the recommendations based on the existing data, and wherein exploring comprises providing the recommendations by selecting a user cluster not assigned a preference weight.