What are the challenges of generating high-quality and understandable explanations?

We conduct research in areas of machine learning with the goal to make member experience better. Our work on recommender systems, contextual bandits, ... Oct 15, 2017 ... Definitely. It's probably considered AI by this point. I have seen OLS described as AI, and if that flies, I can't see anything else that works in the space ... Implementation of Knowledge based Collaborative Filtering and Machine Learning for E-Commerce Recommendation System, Singh, Mahesh Kumar, Rishi, Om Prakash, ... Journal of Machine Learning Research 1 (2000) 49-75. Submitted 5/00; Published 10/00. Dependency Networks for Inference, Collaborative Filtering, and Data ... Carreira-Perpinan, M., & Hinton, G. (2005). On contrastive divergence learning. 10th Int. Work-shop on Artificial Intelligence and Statistics (AISTATS'2005). Collaborative filtering; deep learning; generative adversarial networks; recommender systems. Issue Date: 2019-03. Publisher: IEEE-INST ELECTRICAL ELECTRONICS ... Collaborative filtering. Lecturer: Lester Mackey Date: Oct 15. [Lecture slides]. References. None. The traditional collaborative filtering (CF) suffers from two key ... Machine learning based spam filtering: advantages and challenges. Qi, M. 2010 ... ... Machine Learning. Unit(s): 2. Students study advanced machine learning algorithms, including boosting, collaborative filtering, support vector machines ... Due to the Ising model's strong expressivity and Ising machines' unique computational power, it is highly desired if Ising-based learning can be used in ...