FairRecs

Elevator Pitch: FairRecs leverages the power of GNNs and the innovative AdvDrop framework to deliver bias-mitigated recommendations, ensuring users are exposed to a diverse range of interests truly reflective of their preferences. This not only improves user satisfaction but also boosts the visibility of a wider range of items, creating a more equitable and effective recommendation system.

Concept

A GNN-based recommender system optimized to mitigate biases in user/item interactions, using the Adversarial Graph Dropout (AdvDrop) framework.

Objective

To provide a bias-mitigated, graph-based recommender system that accurately models users’ true preferences.

Solution

FairRecs utilizes the novel AdvDrop framework to separate unbiased genuine interests from biased interactions (e.g., item popularity), ensuring more accurate and fair recommendations.

Revenue Model

Subscription-based for businesses, with tiered pricing based on the volume of data processed and the level of customization required.

Target Market

Online platforms with recommender systems, including e-commerce, streaming services, and content providers.

Expansion Plan

Initially target startups and mid-sized companies, expanding to larger corporations as the technology is proven and refined.

Potential Challenges

Technical integration with diverse platforms, ensuring scalability, and continuous adaptation to emerging biases.

Customer Problem

Current recommender systems often amplify inherent biases, distorting users’ true interests and potentially minimizing the diversity of recommendations.

Regulatory and Ethical Issues

Complying with data protection regulations such as GDPR, ensuring transparency in recommendations, and mitigating unintended bias reinforcement.

Disruptiveness

FairRecs significantly enhances the accuracy and fairness of recommendations, disrupting markets by offering a superior solution to existing biased systems.