{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T00:05:10Z","timestamp":1723421110492},"reference-count":39,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2020,8,1]]},"DOI":"10.1587\/transinf.2019edp7266","type":"journal-article","created":{"date-parts":[[2020,7,31]],"date-time":"2020-07-31T22:16:13Z","timestamp":1596233773000},"page":"1833-1842","source":"Crossref","is-referenced-by-count":1,"title":["Link Prediction Using Higher-Order Feature Combinations across Objects"],"prefix":"10.1587","volume":"E103.D","author":[{"given":"Kyohei","family":"ATARASHI","sequence":"first","affiliation":[{"name":"Hokkaido University"}]},{"given":"Satoshi","family":"OYAMA","sequence":"additional","affiliation":[{"name":"Hokkaido University"},{"name":"RIKEN AIP"}]},{"given":"Masahito","family":"KURIHARA","sequence":"additional","affiliation":[{"name":"Hokkaido University"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] S. Oyama and C.D. Manning, \u201cUsing feature conjunctions across examples for learning pairwise classifiers,\u201d ECML, vol.3201, pp.322-333, 2004. 10.1007\/978-3-540-30115-8_31","DOI":"10.1007\/978-3-540-30115-8_31"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] A. Ben-Hur and W.S. Noble, \u201cKernel methods for predicting protein-protein interactions,\u201d Bioinformatics, vol.21, no.suppl_1, pp.i38-i46, 2005. 10.1093\/bioinformatics\/bti1016","DOI":"10.1093\/bioinformatics\/bti1016"},{"key":"3","unstructured":"[3] W. Wu, Z. Lu, and H. Li, \u201cLearning bilinear model for matching queries and documents,\u201d The Journal of Machine Learning Research, vol.14, no.1, pp.2519-2548, 2013."},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] S. Rendle, \u201cFactorization machines,\u201d ICDM, pp.995-1000, 2010. 10.1109\/icdm.2010.127","DOI":"10.1109\/ICDM.2010.127"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] A.K. Menon and C. Elkan, \u201cLink prediction via matrix factorization,\u201d ECML-PKDD, vol.6912, pp.437-452, 2011. 10.1007\/978-3-642-23783-6_28","DOI":"10.1007\/978-3-642-23783-6_28"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] N. Natarajan and I.S. Dhillon, \u201cInductive matrix completion for predicting gene-disease associations,\u201d Bioinformatics, vol.30, no.12, pp.i60-i68, 2014. 10.1093\/bioinformatics\/btu269","DOI":"10.1093\/bioinformatics\/btu269"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] S. Rendle, \u201cFactorization machines with libfm,\u201d ACM Transactions on Intelligent Systems and Technology, vol.3, no.3, p.57, 2012. 10.1145\/2168752.2168771","DOI":"10.1145\/2168752.2168771"},{"key":"8","unstructured":"[8] M. Blondel, A. Fujino, N. Ueda, and M. Ishihata, \u201cHigher-order factorization machines,\u201d NeurIPS, pp.3351-3359, 2016."},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] X. He and T.-S. Chua, \u201cNeural factorization machines for sparse predictive analytics,\u201d SIGIR, pp.355-364, 2017. 10.1145\/3077136.3080777","DOI":"10.1145\/3077136.3080777"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] Y. Qu, H. Cai, K. Ren, W. Zhang, Y. Yu, Y. Wen, and J. Wang,\u201cProduct-based neural networks for user response prediction,\u201d ICDM, pp.1149-1154, 2016. 10.1109\/icdm.2016.0151","DOI":"10.1109\/ICDM.2016.0151"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] W. Zhang, T. Du, and J. Wang, \u201cDeep learning over multi-field categorical data,\u201d ECIR, vol.9626, pp.45-57, 2016. 10.1007\/978-3-319-30671-1_4","DOI":"10.1007\/978-3-319-30671-1_4"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] H. Guo, R. Tang, Y. Ye, Z. Li, and X. He, \u201cDeepfm: a factorization-machine based neural network for ctr prediction,\u201d IJCAI, pp.1725-1731, 2017. 10.24963\/ijcai.2017\/239","DOI":"10.24963\/ijcai.2017\/239"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] J. Lian, X. Zhou, F. Zhang, Z. Chen, X. Xie, and G. Sun, \u201cxdeepfm: Combining explicit and implicit feature interactions for recommender systems,\u201d KDD, pp.1754-1763, 2018. 10.1145\/3219819.3220023","DOI":"10.1145\/3219819.3220023"},{"key":"14","unstructured":"[14] R. Livni, S. Shalev-Shwartz, and O. Shamir, \u201cOn the computational efficiency of training neural networks,\u201d NeurIPS, pp.855-863, 2014."},{"key":"15","unstructured":"[15] V. Vapnik, Statistical learning theory, Wiley New York, 1998."},{"key":"16","unstructured":"[17] M. Blondel, M. Ishihata, A. Fujino, and N. Ueda, \u201cPolynomial networks and factorization machines: new insights and efficient training algorithms,\u201d ICML, pp.850-858, 2016."},{"key":"17","unstructured":"[18] I. Goodfellow, Y. Bengio, and A. Courville, \u201cDeep learning,\u201d Book in preparation for MIT Press, pp.443-485, 2016."},{"key":"18","unstructured":"[19] M. Fazel, Matrix rank minimization with applications, Ph.D. thesis, PhD thesis, Stanford University, 2002."},{"key":"19","unstructured":"[20] M. Jaggi, M. Sulovsk, et al., \u201cA simple algorithm for nuclear norm regularized problems,\u201d ICML, pp.471-478, 2010."},{"key":"20","doi-asserted-by":"publisher","unstructured":"[21] R. Tibshirani, \u201cRegression shrinkage and selection via the lasso,\u201d Journal of the Royal Statistical Society: Series B (Methodological), vol.58, no.1, pp.267-288, 1996. 10.1111\/j.2517-6161.1996.tb02080.x","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[22] N. Parikh and S. Boyd, \u201cProximal algorithms,\u201d Foundations and Trends\u00ae in Optimization, vol.1, no.3, pp.127-239, 2014. 10.1561\/2400000003","DOI":"10.1561\/2400000003"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[23] K. Atarashi, S. Oyama, M. Kurihara, and K. Furudo, \u201cA deep neural network for pairwise classification: Enabling feature conjunctions and ensuring symmetry,\u201d PAKDD, vol.10234, pp.83-95, 2017. 10.1007\/978-3-319-57454-7_7","DOI":"10.1007\/978-3-319-57454-7_7"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[24] Y. Li, N. Wang, J. Liu, and X. Hou, \u201cFactorized bilinear models for image recognition,\u201d ICCV, pp.2098-2106, 2017. 10.1109\/iccv.2017.229","DOI":"10.1109\/ICCV.2017.229"},{"key":"24","unstructured":"[25] K.W. On, J.h. Kim, J. Kim, and J.w. Ha, \u201cHadamard product for low-rank bilinear pooling,\u201d ICLR, 2017."},{"key":"25","doi-asserted-by":"crossref","unstructured":"[26] X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua, \u201cNeural collaborative filtering,\u201d WWW, pp.173-182, 2017. 10.1145\/3038912.3052569","DOI":"10.1145\/3038912.3052569"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[27] T.-Y. Lin, A. RoyChowdhury, and S. Maji, \u201cBilinear cnn models for fine-grained visual recognition,\u201d ICCV, pp.1449-1457, 2015. 10.1109\/iccv.2015.170","DOI":"10.1109\/ICCV.2015.170"},{"key":"27","doi-asserted-by":"publisher","unstructured":"[28] Y. Koren, R. Bell, and C. Volinsky, \u201cMatrix factorization techniques for recommender systems,\u201d Computer, vol.42, no.8, pp.30-37, 2009. 10.1109\/mc.2009.263","DOI":"10.1109\/MC.2009.263"},{"key":"28","doi-asserted-by":"crossref","unstructured":"[29] A. Grover and J. Leskovec, \u201cnode2vec: Scalable feature learning for networks,\u201d KDD, pp.855-864, 2016. 10.1145\/2939672.2939754","DOI":"10.1145\/2939672.2939754"},{"key":"29","unstructured":"[30] H. Zhao, L. Du, and W. Buntine, \u201cLeveraging node attributes for incomplete relational data,\u201d ICML, pp.4072-4081, 2017."},{"key":"30","unstructured":"[31] Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P.S. Yu, \u201cA comprehensive survey on graph neural networks,\u201d arXiv preprint arXiv:1901.00596, 2019."},{"key":"31","unstructured":"[32] M. Zhang and Y. Chen, \u201cLink prediction based on graph neural networks,\u201d NeurIPS, pp.5165-5175, 2018."},{"key":"32","unstructured":"[33] S. Roweis, \u201chttps:\/\/cs.nyu.edu\/~roweis\/data.html,\u201d 2002."},{"key":"33","unstructured":"[34] GroupLens, \u201chttp:\/\/grouplens.org\/datasets\/movielens\/,\u201d 1998."},{"key":"34","doi-asserted-by":"crossref","unstructured":"[35] A. Novikov, M. Trofimov, and I. Oseledets, \u201cExponential machines. arxiv preprint,\u201d ICLR Workshop., 2017.","DOI":"10.24425\/bpas.2018.125926"},{"key":"35","unstructured":"[36] D. Kingma and J. Ba, \u201cAdam: A method for stochastic optimization,\u201d ICLR, 2014."},{"key":"36","unstructured":"[37] S. Wager, S. Wang, and P.S. Liang, \u201cDropout training as adaptive regularization,\u201d NeurIPS, pp.351-359, 2013."},{"key":"37","doi-asserted-by":"crossref","unstructured":"[38] J. Bromley, J.W. Bentz, L. Bottou, I. Guyon, Y. LeCun, C. Moore, E. S\u00e4ckinger, and R. Shah, \u201cSignature verification using a \u201csiamese\u201d time delay neural network,\u201d NeurIPS, pp.737-744, 1993. 10.1142\/9789812797926_0003","DOI":"10.1142\/9789812797926_0003"},{"key":"38","doi-asserted-by":"crossref","unstructured":"[39] S. Chopra, R. Hadsell, and Y. LeCun, \u201cLearning a similarity metric discriminatively, with application to face verification,\u201d CVPR, pp.539-546, 2005. 10.1109\/cvpr.2005.202","DOI":"10.1109\/CVPR.2005.202"},{"key":"39","doi-asserted-by":"crossref","unstructured":"[40] Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, \u201cDeepface: Closing the gap to human-level performance in face verification,\u201d CVPR, pp.1701-1708, 2014. 10.1109\/cvpr.2014.220","DOI":"10.1109\/CVPR.2014.220"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E103.D\/8\/E103.D_2019EDP7266\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T05:06:18Z","timestamp":1723352778000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E103.D\/8\/E103.D_2019EDP7266\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,1]]},"references-count":39,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2020]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2019edp7266","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"type":"print","value":"0916-8532"},{"type":"electronic","value":"1745-1361"}],"subject":[],"published":{"date-parts":[[2020,8,1]]}}}