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Setting Goals and Choosing Metrics for Recommender System Evaluations

, , and . Proceedings of the Workshop on User-Centric Evaluation of Recommender Systems and Their Interfaces, page 78--85. Chicago, USA, CEUR-WS, (October 2011)

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Pre-Trained Web Table Embeddings for Table Discovery., , , and . aiDM@SIGMOD, page 24-31. ACM, (2021)An ALBERT-based Similarity Measure for Mathematical Answer Retrieval., , and . SIGIR, page 1593-1597. ACM, (2021)Learning from Textual Data in Database Systems., , , and . CIKM, page 375-384. ACM, (2020)FacetE: exploiting web tables for domain-specific word embedding evaluation., , , and . DBTest@SIGMOD, page 5:1-5:6. ACM, (2020)Explore FREDDY: Fast Word Embeddings in Database Systems., , , and . BTW, volume P-289 of LNI, page 529-532. Gesellschaft für Informatik, Bonn, (2019)To Softmax, or not to Softmax: that is the question when applying Active Learning for Transformer Models., , , , , and . CoRR, (2022)ImitAL: Learning Active Learning Strategies from Synthetic Data., , and . CoRR, (2021)Multi-Objective Scheduling for Real-Time Data Warehouses., , and . BTW, volume P-144 of LNI, page 307-326. GI, (2009)TU_DBS in the ARQMath Lab 2021, CLEF., , and . CLEF (Working Notes), volume 2936 of CEUR Workshop Proceedings, page 107-124. CEUR-WS.org, (2021)Transformer-Encoder-Based Mathematical Information Retrieval., , and . CLEF, volume 13390 of Lecture Notes in Computer Science, page 175-189. Springer, (2022)