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Bridging the Utilitarian-Hedonic Divide in Crowdsourcing Applications.

, , , , and . CrowdMM@ACM Multimedia, page 9-14. ACM, (2015)

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The Benchmarking Initiative for Multimedia Evaluation: MediaEval 2016., , , , and . IEEE Multim., 24 (1): 93-96 (2017)Partially Synthetic Data for Recommender Systems: Prediction Performance and Preference Hiding., , and . CoRR, (2020)Bridging the Utilitarian-Hedonic Divide in Crowdsourcing Applications., , , , and . CrowdMM@ACM Multimedia, page 9-14. ACM, (2015)Overview of the 2015 Workshop on Speech, Language and Audio in Multimedia., , , and . ACM Multimedia, page 1347-1348. ACM, (2015)Reading between the tags to predict real-world size-class for visually depicted objects in images., , and . ACM Multimedia, page 273-282. ACM, (2011)RecSys Challenge 2016: Job Recommendations., , , , and . RecSys, page 425-426. ACM, (2016)'Free lunch' enhancement for collaborative filtering with factorization machines., , , and . RecSys, page 281-284. ACM, (2014)Spoken content retrieval: Searching spontaneous conversational speech., , , , and . SIGIR Forum, 42 (2): 66-75 (2008)Unifying rating-oriented and ranking-oriented collaborative filtering for improved recommendation., , and . Inf. Sci., (2013)Special Issue on Information Access to Cultural Heritage., , and . J. Digit. Inf., (2009)