Here are the machine learning research I have done in graduate school. I also have a fresh Google Scholar account as well :)

MetaRec: Meta-Learning Meets RecSys

Artificial neural networks (ANNs) have recently received increasing attention as powerful modeling tools to improve the performance of recommendation systems. Meta-learning, on the other hand, is a paradigm that has re-surged in popularity within the broader machine learning community over the past several years. In this thesis, we will explore the intersection of these two domains and work on developing methods for integrating meta-learning to design more accurate and flexible recommendation systems.

In the present work, we propose a meta-learning framework for the design of collaborative filtering methods in recommendation systems, drawing from ideas, models, and solutions from modern approaches in both the meta-learning and recommendation system literature, applying them to recommendation tasks to obtain improved generalization performance.

Our proposed framework, MetaRec, includes and unifies the main state-of-the-art models in recommendation systems, extending them to be flexibly configured and efficiently operate with limited data.

We empirically test the architectures created under our MetaRec framework on several recommendation benchmark datasets using a plethora of evaluation metrics and find that by taking a meta-learning approach to the collaborative filtering problem, we observe notable gains in predictive performance.

Meta-Learning Is All You Need

Neural networks have been highly influential in the past decades in the machine learning community, thanks to the rise of compute power, the abundance of unstructured data, and the advancement of algorithmic solutions. However, it is still a long way for researchers to completely use neural networks in real-world settings where the data is scarce and requirements for model accuracy/speed are critical.

Meta-learning, also known as learning how to learn, has recently emerged as a potential learning paradigm that can learn information from one task and generalize that information to unseen tasks proficiently. In this report, the key questions that I attempt to answer are: (1) Why do we need meta-learning?, (2) How does the math of meta-learning work?, and (3) What are the different approaches to design a meta-learning algorithm?

From Matrix Factorization To Deep Neural Networks: The Evolution Of Collaborative Filtering Solutions for Recommendation Systems

Recommendation systems are technologies and techniques that can provide recommendations for items to be of use to a user. The recommendations provided are aimed at supporting their users in various decision-making processes, such as what products to purchase, what music to listen, or what routes to take. Correspondingly, various techniques for recommendation generation have been proposed and deployed in commercial environments. The goal of this independent study is to impose a degree of order upon this diversity by presenting a coherent and unified repository of the most common recommendation methods to solve the collaborative filtering problem: from classic matrix factorization to cutting-edge deep neural networks.

Travel Time Optimization Problem via Ant Colony and genetic evolution

Using a collection of New York City locations represented as longitude and latitude coordinates, we search the solution space to find the most efficient traveling salesman path. By creating a regression model to solve the travel time cost between two locations, we optimize the traveling salesman path using two biologically inspired meta-heuristics: ant colony and genetic evolution. Our experimentation shows that ant colony optimization performs better 30% better than genetic evolution.

Clothing Retrieval and Visual Recommendation for Fashion Images

The fashion domain is a very popular playground for applications of machine learning and computer vision. The problems in this domain is challenging due to the high level of subjectivity and the semantic complexity of the features involved. Recent work has focused on many approaches including attribute recognition, clothing retrieval, image generation, and visual recommendation. In this project, I developed a two-stage deep learning framework that can retrieve clothing images from the data and then visually recommend similar images for specific fashion styles. I trained my model on the Fashion144k dataset and tested it on the DeepFashion dataset. The experiments demonstrate the effectiveness of my model.