User-Powered Recommendation System With Privacy-Preserving Sampling
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Solution Overview
Problem
Current recommendation systems rely on collaborative filtering, which may limit users' exposure to diverse recommendations and fail to preserve user privacy, as they do not allow users to selectively choose communities or algorithms for recommendations.
Innovation Solution
A user-defined recommendation system that allows users to specify community definitions, item constraints, aggregation functions, and algorithms, while incorporating data perturbation for privacy protection and progressive sampling for efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If collaborative filtering techniques are used to automatically identify communities, then recommendation automation is improved, but user control over community selection deteriorates
Solution Approach 1:
The system segments the recommendation process into distinct controllable components: users can select specific communities from multiple available communities, choose different aggregation functions (max, min, sum, average), and pick various selection algorithms. This segmentation allows both automation and user control to coexist by dividing the system into automated community identification modules and user-selectable preference modules.
2Measurement precision
If users associate only with similar people for recommendations, then recommendation relevance is improved, but diversity of recommendations deteriorates
Solution Approach 1:
The system dynamically adjusts the composition of communities used for recommendations. Users can dynamically select which communities to include or exclude based on their current needs. The system supports switching between different aggregation functions and selection algorithms dynamically, allowing users to explore diverse recommendations when desired while maintaining relevance when needed.
3Measurement precision
If rating data is collected from community members, then recommendation accuracy is improved, but user privacy deteriorates
Solution Approach 1:
The system introduces an intermediary layer between user rating data and the recommendation generation process. This intermediary implements privacy-preserving techniques such as data perturbation, where rating data is modified or obscured before being used in calculations. The intermediary allows the system to leverage community rating data for accurate recommendations while protecting individual user privacy through cryptographic or statistical methods.
Data Source
AI summary
Recommendation systems are widely used in Internet applications. In current recommendation systems, users only play a passive role and have limited control over the recommendation generation process. As a result, there is often considerable mismatch between the recommendations made by these systems and the actual user interests, which are fine-grained and constantly evolving. With a user-powered distributed recommendation architecture, individual users can flexibly define fine-grained communities of interest in a declarative fashion and obtain recommendations accurately tailored to their interests by aggregating opinions of users in such communities. By combining a progressive sampling technique with data perturbation methods, the recommendation system is both scalable and privacy-preserving.