{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:44:30Z","timestamp":1740123870085,"version":"3.37.3"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2017,10,6]],"date-time":"2017-10-06T00:00:00Z","timestamp":1507248000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"National Basic Research 973 Program of China","award":["2015CB352403"],"award-info":[{"award-number":["2015CB352403"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61602301"],"award-info":[{"award-number":["61602301"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Parallel Prog"],"published-print":{"date-parts":[[2018,8]]},"DOI":"10.1007\/s10766-017-0525-y","type":"journal-article","created":{"date-parts":[[2017,10,6]],"date-time":"2017-10-06T07:54:06Z","timestamp":1507276446000},"page":"686-698","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DCF: A Dataflow-Based Collaborative Filtering Training Algorithm"],"prefix":"10.1007","volume":"46","author":[{"given":"Xiangyu","family":"Ju","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenning","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minyi","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guang R.","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,10,6]]},"reference":[{"key":"525_CR1","unstructured":"Apache hadoop project. http:\/\/hadoop.apache.org\/ (2017)"},{"key":"525_CR2","unstructured":"Abadi, M., Barham, P., et\u00a0al.: Tensorflow: a system for large-scale machine learning. In: OSDI. Savannah, Georgia, USA (2016)"},{"issue":"6","key":"525_CR3","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TKDE.2005.99","volume":"17","author":"G Adomavicius","year":"2005","unstructured":"Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734\u2013749 (2005)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"525_CR4","doi-asserted-by":"crossref","unstructured":"Armbrust, M., Xin, R.S., et\u00a0al.: Spark sql: relational data processing in spark. In: SIGMOD, pp. 1383\u20131394. ACM (2015)","DOI":"10.1145\/2723372.2742797"},{"key":"525_CR5","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1002\/cpe.1631","volume":"23","author":"C Augonnet","year":"2011","unstructured":"Augonnet, C., Thibault, S., Namyst, R., Wacrenier, P.A.: StarPU: a unified platform for task scheduling on heterogeneous multicore architectures. Euro-Par 23, 187\u2013198 (2011). doi: 10.1002\/cpe.1631","journal-title":"Euro-Par"},{"key":"525_CR6","first-page":"109","volume":"46","author":"J Bobadilla","year":"2013","unstructured":"Bobadilla, J., Ortega, F., Hernando, A., Guti\u00e9rrez, A.: Knowledge-based systems. Recomm. Syst. Surv. 46, 109\u2013132 (2013)","journal-title":"Recomm. Syst. Surv."},{"key":"525_CR7","unstructured":"Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43\u201352. Morgan Kaufmann Publishers Inc. (1998)"},{"issue":"1","key":"525_CR8","first-page":"2","volume":"6","author":"WS Chin","year":"2015","unstructured":"Chin, W.S., Zhuang, Y., Juan, Y.C., Lin, C.J.: A fast parallel stochastic gradient method for matrix factorization in shared memory systems. ACM Trans. Intell. Syst. Technol. (TIST) 6(1), 2 (2015)","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"525_CR9","unstructured":"Culler, D.E.: Dataflow architectures. Technical report, DTIC Document (1986)"},{"key":"525_CR10","unstructured":"Dean, J., Corrado, G., et\u00a0al.: Large scale distributed deep networks. In: Advances in Neural Information Processing Systems, pp. 1223\u20131231 (2012)"},{"issue":"1","key":"525_CR11","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1145\/1327452.1327492","volume":"51","author":"J Dean","year":"2008","unstructured":"Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107\u2013113 (2008)","journal-title":"Commun. ACM"},{"key":"525_CR12","doi-asserted-by":"crossref","unstructured":"Gemulla, R., Nijkamp, E., Haas, P.J., Sismanis, Y.: Large-scale matrix factorization with distributed stochastic gradient descent. In: SIGKDD, pp. 69\u201377. ACM (2011)","DOI":"10.1145\/2020408.2020426"},{"key":"525_CR13","doi-asserted-by":"crossref","unstructured":"Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: ICDM, pp. 263\u2013272. IEEE (2008)","DOI":"10.1109\/ICDM.2008.22"},{"key":"525_CR14","doi-asserted-by":"crossref","unstructured":"Kim, J.K., Ho, Q., Lee, S., Zheng, X., Dai, W., Gibson, G.A., Xing, E.P.: Strads: a distributed framework for scheduled model parallel machine learning. In: Eurosys, p.\u00a05 (2016)","DOI":"10.1145\/2901318.2901331"},{"issue":"8","key":"525_CR15","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MC.2009.263","volume":"42","author":"Y Koren","year":"2009","unstructured":"Koren, Y., Bell, R., Volinsky, C., et al.: Matrix factorization techniques for recommender systems. Computer 42(8), 30\u201337 (2009)","journal-title":"Computer"},{"key":"525_CR16","doi-asserted-by":"crossref","unstructured":"Li, M., Andersen, D.G., et\u00a0al.: Scaling distributed machine learning with the parameter server. In: OSDI, vol. 1, p. 3 (2014)","DOI":"10.1145\/2640087.2644155"},{"key":"525_CR17","unstructured":"Low, Y., Gonzalez, J.E., Kyrola, A., Bickson, D., Guestrin, C.E., Hellerstein, J.: Graphlab: a new framework for parallel machine learning. arXiv preprint arXiv:1408.2041 (2014)"},{"issue":"34","key":"525_CR18","first-page":"1","volume":"17","author":"X Meng","year":"2016","unstructured":"Meng, X., Bradley, J., et al.: Mllib: machine learning in apache spark. JMLR 17(34), 1\u20137 (2016)","journal-title":"JMLR"},{"key":"525_CR19","doi-asserted-by":"crossref","unstructured":"Oh, J., Han, W.S., Yu, H., Jiang, X.: Fast and robust parallel SGD matrix factorization. In: SIGKDD, pp. 865\u2013874. ACM (2015)","DOI":"10.1145\/2783258.2783322"},{"issue":"1","key":"525_CR20","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/BF02293745","volume":"42","author":"Y Takane","year":"1977","unstructured":"Takane, Y., Young, F.W., De Leeuw, J.: Nonmetric individual differences multidimensional scaling: an alternating least squares method with optimal scaling features. Psychometrika 42(1), 7\u201367 (1977)","journal-title":"Psychometrika"},{"key":"525_CR21","doi-asserted-by":"crossref","unstructured":"Zuckerman, S., Suetterlein, J., Knauerhase, R., Gao, G.R.: Using a codelet program execution model for exascale machines: position paper. In: Proceedings of the 1st International Workshop on Adaptive Self-Tuning Computing Systems for the Exaflop Era, pp. 64\u201369. ACM (2011)","DOI":"10.1145\/2000417.2000424"}],"container-title":["International Journal of Parallel Programming"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10766-017-0525-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10766-017-0525-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10766-017-0525-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,4]],"date-time":"2019-10-04T05:44:43Z","timestamp":1570167883000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10766-017-0525-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,6]]},"references-count":21,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2018,8]]}},"alternative-id":["525"],"URL":"https:\/\/doi.org\/10.1007\/s10766-017-0525-y","relation":{},"ISSN":["0885-7458","1573-7640"],"issn-type":[{"type":"print","value":"0885-7458"},{"type":"electronic","value":"1573-7640"}],"subject":[],"published":{"date-parts":[[2017,10,6]]}}}