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Learning representations for quality estimation of crowdsourced submissions.

, , , and . Inf. Process. Manag., 56 (4): 1484-1493 (2019)

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Learning representations for quality estimation of crowdsourced submissions., , , and . Inf. Process. Manag., 56 (4): 1484-1493 (2019)Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction., , , , , , , and . SIGIR, page 1157-1166. ACM, (2021)Contrastive Learning for Conversion Rate Prediction., , , , , , and . SIGIR, page 1909-1913. ACM, (2023)Masked Multi-Domain Network: Multi-Type and Multi-Scenario Conversion Rate Prediction with a Single Model., , , , , , , , , and 1 other author(s). CoRR, (2024)Representation Learning-Assisted Click-Through Rate Prediction., , , , , and . IJCAI, page 4561-4567. ijcai.org, (2019)What We Vote for? Answer Selection from User Expertise View in Community Question Answering., , , , and . WWW, page 1198-1209. ACM, (2019)Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction., , , , , , and . KDD, page 2078-2086. ACM, (2019)Masked Multi-Domain Network: Multi-Type and Multi-Scenario Conversion Rate Prediction with a Single Model., , , , , , , , , and 1 other author(s). CIKM, page 4773-4779. ACM, (2023)Truth Discovery by Claim and Source Embedding., , , , and . IEEE Trans. Knowl. Data Eng., 33 (3): 1264-1275 (2021)Click-Through Rate Prediction with the User Memory Network., , , , , and . CoRR, (2019)