Personal Assistant Systems
42d6c7d61481d1c21bd1635f59edae05-Paper.pdf
Voting systems are highly prevalent in our daily lives. Examples range from large scale democratic elections to company or family-wide decision making, recommender systems and product design[Boutilieretal.,2015].
A/B Testing for Recommender Systems in a Two-sided Marketplace
A/B testing is a powerful tool because of its design simplicity and ease of setup.
UniCoRn_with_appendix
A/B testing is a powerful tool because of its design simplicity and ease of setup.
Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss An Zhang
An Zhang and Leheng Sheng contribute equally to this work. Zhibo Cai is the corresponding author.
1c446a652e50b1ea5618b66c07bfc0c5-Supplemental-Conference.pdf
While other areas of machine learning have seen more and more automation, designing a high-performing recommender system still requires a high level of human effort. Furthermore, recent work has shown that modern recommender system algorithms do not always improve over well-tuned baselines. A natural follow-up question is, "how do we choose the right algorithm for anew dataset and performance metric?" In this work, we start by giving the first large-scale study ofrecommender system approaches bycomparing 24algorithms and100 sets of hyperparameters across 85 datasets and 315 metrics. We find that the best algorithms and hyperparameters are highly dependent on the dataset and performance metric.
