138 citations · 265 across the 17 of their papers we have counts for
4 papers · 1 filter
Lightweight Compositional Embeddings for Incremental Streaming Recommendation
Mengyue Hang, Tobias Schnabel, Longqi Yang +1
Most work in graph-based recommender systems considers a {\em static} setting where all information about test nodes (i.e., users and items) is available upfront at training time.…
Effective Evaluation using Logged Bandit Feedback from Multiple Loggers
Aman Agarwal, Soumya Basu, Tobias Schnabel +1
Accurately evaluating new policies (e.g. ad-placement models, ranking functions, recommendation functions) is one of the key prerequisites for improving interactive systems. While…
Recommendations as Treatments: Debiasing Learning and Evaluation
Tobias Schnabel, Adith Swaminathan, Ashudeep Singh +2
Most data for evaluating and training recommender systems is subject to selection biases, either through self-selection by the users or through the actions of the recommendation sy…
Towards a Better Understanding of Predict and Count Models
S. Sathiya Keerthi, Tobias Schnabel, Rajiv Khanna
In a recent paper, Levy and Goldberg pointed out an interesting connection between prediction-based word embedding models and count models based on pointwise mutual information. Un…