138 citations · 265 across the 17 of their papers we have counts for
17 papers
When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?
Yushun Dong, Jundong Li, Tobias Schnabel
In recent years, neural models have been repeatedly touted to exhibit state-of-the-art performance in recommendation. Nevertheless, multiple recent studies have revealed that the r…
Situating Recommender Systems in Practice: Towards Inductive Learning and Incremental Updates
Tobias Schnabel, Mengting Wan, Longqi Yang
With information systems becoming larger scale, recommendation systems are a topic of growing interest in machine learning research and industry. Even though progress on improving…
Where Do We Go From Here? Guidelines For Offline Recommender Evaluation
Tobias Schnabel
Various studies in recent years have pointed out large issues in the offline evaluation of recommender systems, making it difficult to assess whether true progress has been made. H…
EvalRS: a Rounded Evaluation of Recommender Systems
Jacopo Tagliabue, Federico Bianchi, Tobias Schnabel +4
Much of the complexity of Recommender Systems (RSs) comes from the fact that they are used as part of more complex applications and affect user experience through a varied range of…
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.…
SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization
Philippe Laban, Tobias Schnabel, Paul N. Bennett +1
In the summarization domain, a key requirement for summaries is to be factually consistent with the input document. Previous work has found that natural language inference (NLI) mo…
Keep it Simple: Unsupervised Simplification of Multi-Paragraph Text
Philippe Laban, Tobias Schnabel, Paul Bennett +1
This work presents Keep it Simple (KiS), a new approach to unsupervised text simplification which learns to balance a reward across three properties: fluency, salience and simplici…
Deep Generalized Method of Moments for Instrumental Variable Analysis
Andrew Bennett, Nathan Kallus, Tobias Schnabel
Instrumental variable analysis is a powerful tool for estimating causal effects when randomization or full control of confounders is not possible. The application of standard metho…
Improving Recommender Systems Beyond the Algorithm
Tobias Schnabel, Paul N. Bennett, Thorsten Joachims
Recommender systems rely heavily on the predictive accuracy of the learning algorithm. Most work on improving accuracy has focused on the learning algorithm itself. We argue that t…
A preference elicitation interface for collecting dense recommender datasets with rich user information
Pantelis P. Analytis, Tobias Schnabel, Stefan Herzog +2
We present an interface that can be leveraged to quickly and effortlessly elicit people's preferences for visual stimuli, such as photographs, visual art and screensavers, along wi…
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…
Unbiased Learning-to-Rank with Biased Feedback
Thorsten Joachims, Adith Swaminathan, Tobias Schnabel
Implicit feedback (e.g., clicks, dwell times, etc.) is an abundant source of data in human-interactive systems. While implicit feedback has many advantages (e.g., it is inexpensive…
Unbiased Comparative Evaluation of Ranking Functions
Tobias Schnabel, Adith Swaminathan, Peter Frazier +1
Eliciting relevance judgments for ranking evaluation is labor-intensive and costly, motivating careful selection of which documents to judge. Unlike traditional approaches that mak…
Online Updating of Word Representations for Part-of-Speech Tagging
Wenpeng Yin, Tobias Schnabel, Hinrich Schütze
We propose online unsupervised domain adaptation (DA), which is performed incrementally as data comes in and is applicable when batch DA is not possible. In a part-of-speech (POS)…
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…
Using Shortlists to Support Decision Making and Improve Recommender System Performance
Tobias Schnabel, Paul N. Bennett, Susan T. Dumais +1
In this paper, we study shortlists as an interface component for recommender systems with the dual goal of supporting the user's decision process, as well as improving implicit fee…