Human

ML AND MATCHING ALGORITHM RESEARCH

SCOPE OF PROJECT

ML & Matching Algorithm Research

Timeline: March 2022 - June 2022

What We Did:  Analyzed Human’s existing ML models for matching users, evaluated unsupervised learning techniques, clustering methodologies, and collaborative filtering approaches

Tools: Python, scikit-learn, NLP, Deep Learning Models

Goal of Project

  • Understand and compare ML-driven matchmaking approaches.

  • Identify key strengths and limitations of different algorithms.

  • Assess how clustering and classification models impact user experience.

  • Investigate ethical concerns and biases in ML-based matchmaking.

  • Explore deep learning techniques for personalized recommendations.

Guiding Questions

  • How can clustering improve recommendation systems for human relationships?

  • What role does NLP play in processing bios and user preferences?

  • Can we apply collaborative filtering similar to e-commerce recommendations?

  • How do preference-based algorithms like Gale-Shapley impact user satisfaction?

  • How can we mitigate biases present in ML-driven matchmaking systems?

  • Can neural networks improve matchmaking accuracy over traditional clustering?

Description of Project

The goal of this project is to explore and analyze various machine learning approaches used for human-matching applications, with a particular focus on dating algorithms. We examined unsupervised learning models, clustering methodologies, and preference-based algorithms to understand how different systems determine compatibility and optimize user engagement.

Our Findings

We started by reviewing different unsupervised learning techniques, including clustering models such as K-Means, hierarchical clustering, and Gaussian mixture models. We analyzed their effectiveness in grouping users based on similarities. Next, we examined dating-specific algorithms, including the use of NLP for bio analysis and collaborative filtering for user preference matching. Additionally, we explored the impact of Gale-Shapley’s stable matching algorithm and how platforms like Tinder, Hinge, and OkCupid leverage ML to optimize user experiences.

Phase 1: Clustering algorithms help group users based on shared attributes but require careful tuning of hyperparameters.

Phase 2: Collaborative filtering leverages user interactions to refine recommendations, similar to e-commerce suggestions.

Phase 3: Gale-Shapley algorithm ensures stable matches but does not consider external factors such as user engagement trends.

Phase 4: Bias concerns in ML-based matchmaking arise from imbalanced datasets and algorithmic reinforcement of stereotypes.

Phase 5: Neural networks can improve matchmaking by learning complex user behaviors but require large amounts of training data.

Our Impact

Understanding these algorithms can help improve user engagement, retention, and overall satisfaction in human-matching applications. By optimizing clustering methods and leveraging preference-based algorithms, companies can offer better matchmaking experiences while addressing ethical concerns. Implementing deep learning techniques can further enhance personalized recommendations and improve user compatibility predictions.

Reflection

This research highlights the strengths and weaknesses of different ML techniques for human matching. While clustering and NLP enhance compatibility detection, biases in training data can impact fairness. Future research should focus on refining ethical AI approaches to ensure inclusive and unbiased matchmaking. Deep learning models hold promise for more nuanced user preference predictions but require careful implementation to prevent reinforcing biases.