Designing data sharing mechanisms providing performance and strong privacy guarantees is a hot topic for the Online Advertising industry.
Advances in computer vision are pushing the limits of im-age manipulation, with generative models sampling detailed images on various tasks.
This task requires fitting an in-shop cloth image on the image of a person, which is highly challenging because it involves cloth warping, image compositing, and synthesizing.
Our formulation results in a efficient algorithm that accounts for a simple re-weighting of policy actions in the standard policy iteration scheme.
A common assumption in recommender systems (RS) is the existence of a best fixed recommendation strategy.
Recent breakthroughs in computer vision and natural language processing have spurred interest in challenging multi-modal tasks such as visual question-answering and visual dialogue.
Recommender systems objectives can be broadly characterized as modeling user preferences over short-or long-term time horizon.
Determinantal point processes (DPPs) have received significant attention in the recent years as an elegant model for a variety of machine learning tasks, due to their ability to elegantly model set diversity and item quality or popularity.
It is commonly assumed that language refers to high-level visual concepts while leaving low-level visual processing unaffected.
A standard model for Recommender Systems is the Matrix Completion setting: given partially known matrix of ratings given by users (rows) to items (columns), infer the unknown ratings.
Ranked #1 on Recommendation Systems on Douban
Finally, experimental evidence confirm that our algorithm is effective in dealing with the cold start problem on publicly available datasets.
The algorithms that we construct for solving these problems are based on a new metric between time-series distributions, which can be evaluated using binary classification methods.