Preferences and Recommendations from Data & AI
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...
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...
During the teaching appointment at Montana State University, Robert M. Pirsig, the author of “Zen and the Art of Motorcycle Maintenance“, conducted a following...
On 11 September 2019, Andrew successfully passed the defense of his PhD dissertation, entitled “Preference Learning and Similarity Learning Perspectives on Personalized Recommendation”. He...
In August 2019, Andrew attended the 28th International Joint Conference on Artificial Intelligence (IJCAI-2019). He shares his learning and experience in this blog post....
Preferred.AI held a TechFest on Friday 23 August 2019, 3.30pm – 6.30pm to share the recent projects that we’re actively pursuing. Captivating talks based...
Data sparsity is one of the biggest challenges in recommender systems. A promising solution to alleviate this problem is to rely on auxiliary sources...
In May 2019, Hady traveled to San Francisco for the Web Conference 2019 (previously the conference series was known as World Wide Web or...
Here in Preferred.AI, much of our work involves processing and manipulating data. We regularly find ourselves wanting to explore a given dataset quickly and...
On 22 April 2019, Preferred.AI minted a new PhD. Trong Le passed the defense of his PhD dissertation, entitied “Modeling Sequential and Basket-Oriented Associations...
