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Forgetting techniques for stream-based matrix factorization in recommender systems

, , , , and . Knowledge and Information Systems, (Aug 4, 2017)
DOI: 10.1007/s10115-017-1091-8

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Selektives Lernen für Empfehlungsmaschinen.. Ausgezeichnete Informatikdissertationen, volume D-18 of LNI, GI, (2017)Scalable Online Top-N Recommender Systems, , , , , , and . page 3--20. Springer International Publishing, (2017)Forgetting techniques for stream-based matrix factorization in recommender systems, , , , and . Knowledge and Information Systems, (Aug 4, 2017)Entity-Level Stream Classification: Exploiting Entity Similarity to Label the Future Observations Referring to an Entity., , , , , , , and . DSAA, page 246-255. IEEE, (2018)Personalized recommender systems for product-line configuration processes, , , , and . Computer Languages, Systems & Structures, (2018)Entity-level stream classification: exploiting entity similarity to label the future observations referring to an entity, , , , , , , and . International Journal of Data Science and Analytics, (2019)Exploiting entity information for stream classification over a stream of reviews., , , , , and . SAC, page 564-573. ACM, (2019)Prediction of surgery duration using empirical anesthesia protocols, , and . The first International Workshop on Knowledge Discovery in Health Care and Medicine. - Athen, (2011)Entity-level stream classification: exploiting entity similarity to label the future observations referring to an entity., , , , , , , and . Int. J. Data Sci. Anal., 9 (1): 1-15 (2020)ORSUM Chairs' Welcome & Organization., , , and . WWW (Companion Volume), page 1365-1366. ACM, (2018)