4.4k citations · 4.4k across the 5 of their papers we have counts for
3 papers · 1 filter
Answering Compositional Queries with Set-Theoretic Embeddings
Shib Dasgupta, Andrew McCallum, Steffen Rendle +1
The need to compactly and robustly represent item-attribute relations arises in many important tasks, such as faceted browsing and recommendation systems. A popular machine learnin…
A Generic Coordinate Descent Framework for Learning from Implicit Feedback
Immanuel Bayer, Xiangnan He, Bhargav Kanagal +1
In recent years, interest in recommender research has shifted from explicit feedback towards implicit feedback data. A diversity of complex models has been proposed for a wide vari…
BPR: Bayesian Personalized Ranking from Implicit Feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner +1
Item recommendation is the task of predicting a personalized ranking on a set of items (e.g. websites, movies, products). In this paper, we investigate the most common scenario wit…