166 citations · 346 across the 9 of their papers we have counts for
4 papers · 1 filter
Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning
Dusan Stamenkovic, Alexandros Karatzoglou, Ioannis Arapakis +2
Since the inception of Recommender Systems (RS), the accuracy of the recommendations in terms of relevance has been the golden criterion for evaluating the quality of RS algorithms…
Learning Robust Recommenders through Cross-Model Agreement
Yu Wang, Xin Xin, Zaiqiao Meng +3
Learning from implicit feedback is one of the most common cases in the application of recommender systems. Generally speaking, interacted examples are considered as positive while…
AutoDebias: Learning to Debias for Recommendation
Jiawei Chen, Hande Dong, Yang Qiu +5
Recommender systems rely on user behavior data like ratings and clicks to build personalization model. However, the collected data is observational rather than experimental, causin…
Graph Convolutional Embeddings for Recommender Systems
Paula Gómez Duran, Alexandros Karatzoglou, Jordi Vitrià +2
Modern recommender systems (RS) work by processing a number of signals that can be inferred from large sets of user-item interaction data. The main signal to analyze stems from the…