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...
During the teaching appointment at Montana State University, Robert M. Pirsig, the author of “Zen and the Art of Motorcycle Maintenance“, conducted a following...
Data sparsity is one of the biggest challenges in recommender systems. A promising solution to alleviate this problem is to rely on auxiliary sources...
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...
In this age where new content is generated on the Web every second, it is only natural that we find ways to harness them....
Building on the success of the premiere of Preferred.AI-designed enrichment course on Web Data Extraction & Regression Analysis Using Java, we staged another run...
Upon the invitation from NUS High School of Mathematics and Science, Tuan and Hady interacted with a group of 24 eager and precocious students...
Fired with an educational zeal, Preferred.AI conducted a 5-day (Oct 3-8 2018) enrichment course on Web Data Extraction and Regression Analysis, which was organized...
Our appreciation to KDD.SG (Singapore Chapter of SIGKDD) and DSSG (DataScience Singapore) for jointly organizing and inviting us to deliver a public tutorial on...
