Thorsten Joachims
7 papers in the PaperMetrix corpus
Papers by this author
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Unbiased Comparative Evaluation of Ranking Functions
2016 · arXiv (Cornell University)
Eliciting relevance judgments for ranking evaluation is labor-intensive and costly, motivating careful selection of which documents to judge. Unlike traditional approaches that make this selection deterministically, probabilistic sampling has shown intriguing promise since it enables …
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Language-Based User Profiles for Recommendation
2024 · arXiv (Cornell University)
Most conventional recommendation methods (e.g., matrix factorization) represent user profiles as high-dimensional vectors. Unfortunately, these vectors lack interpretability and steerability, and often perform poorly in cold-start settings. To address these shortcomings, we explore the use …
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End-to-end Training for Recommendation with Language-based User Profiles
2024 · arXiv (Cornell University)
There is a growing interest in natural language-based user profiles for recommender systems, which aims to enhance transparency and scrutability compared with embedding-based methods. Existing studies primarily generate these profiles using zero-shot inference from large …
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Evaluation methods for unsupervised word embeddings
2015
We present a comprehensive study of evaluation methods for unsupervised embedding techniques that obtain meaningful representations of words from text. Different evaluations result in different orderings of embedding methods, calling into question the common assumption …
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Recommendations as Treatments: Debiasing Learning and Evaluation
2016 · arXiv (Cornell University)
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 system itself. In this paper, we provide a principled …
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Unbiased Learning-to-Rank with Biased Feedback
2017
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 to collect, user centric, and timely), its inherent biases …
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Accurately Interpreting Clickthrough Data as Implicit Feedback
2017 · ACM SIGIR Forum
This paper examines the reliability of implicit feedback generated from clickthrough data in WWW search. Analyzing the users' decision process using eyetracking and comparing implicit feedback against manual relevance judgments, we conclude that clicks are …