Multi-Modal Recommender Systems: Hands-On Exploration
Tuan, Aghiles, and Hady will be delivering a tutorial at the RecSys-21 conference that will take place in September 2021. Abstract Recommender systems typically...
Recent Posts
Fast and Accurate Personalized Recommendation with Indexing
In a previous blog post, we introduce the problem of top-K recommendation retrieval using Matrix Factorization (MF). We also highlight the importance of indexing structures as a...
A Quest for Fast Personalized Recommendation
Personalized recommender systems attempt to generate a limited number of item options (e.g., products on Amazon, movies on Netflix, or videos on Youtube, etc.)...
Zooming in on TechFest 2020
Join us for TechFest 2020 by Preferred.AI! Our theme this year is Zooming In. Inside an innovative app is a cool AI algorithm. On...
Visual Sentiment Analysis: A Need for Context
Previously, we gave our readers a glance at visual sentiment analysis in an earlier blog post. The goal of the task was seeking the...
Neural Topic Modeling for Document Networks with Adjacent-Encoder
Text documents are usually linked to one another. For example, academic papers cite other papers, web pages link to other pages, etc. Most of...
Why maximum likelihood-based training may lead to the generation of unrealistic samples
Generative models specify procedures allowing us to produce data samples, e.g., images, texts, user preferences, etc. These models are central to unsupervised learning, and...
Call for Interns 2020
Preferred.AI is calling for internship applications from current students of SMU to work with us during the summer (May to July 2020) on a...
The Zen of Comparisons
During the teaching appointment at Montana State University, Robert M. Pirsig, the author of “Zen and the Art of Motorcycle Maintenance“, conducted a following...
Gaining Perspective
On 11 September 2019, Andrew successfully passed the defense of his PhD dissertation, entitled “Preference Learning and Similarity Learning Perspectives on Personalized Recommendation”. He...
