{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T21:40:05Z","timestamp":1692826805602},"reference-count":31,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2017]]},"DOI":"10.1587\/transinf.2016edp7421","type":"journal-article","created":{"date-parts":[[2017,5,31]],"date-time":"2017-05-31T22:23:56Z","timestamp":1496269436000},"page":"1242-1250","source":"Crossref","is-referenced-by-count":1,"title":["A Novel Embedding Model for Relation Prediction in Recommendation Systems"],"prefix":"10.1587","volume":"E100.D","author":[{"given":"Yu","family":"ZHAO","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunications"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheng","family":"GAO","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick","family":"GALLINARI","sequence":"additional","affiliation":[{"name":"LIP6, University of Paris VI"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"GUO","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] G. Adomavicius and A. Tuzhilin, \u201cToward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,\u201d Proc. IEEE Trans. Knowl. Data Eng., vol.17, no.6, pp.734-749, 2005.","DOI":"10.1109\/TKDE.2005.99"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] S. Bhagat, U. Weinsberg, S. Ioannidis, and N. Taft, \u201cRecommending with an agenda: Active learning of private attributes using matrix factorization,\u201d Proc. Recsys, pp.65-72, 2014.","DOI":"10.1145\/2645710.2645747"},{"key":"3","unstructured":"[3] A. Bordes, N. Usunier, and A. Garcia-Duran, \u201cTranslating embeddings for modeling multi-relational data,\u201d Proc. NIPS, pp.2787-2795, 2013."},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] A. Bordes, J. Weston, R. Collobert, and Y. Bengio, \u201cLearning structured embeddings of knowledge bases,\u201d Proc. 25th Annual Conference on Artificial Intelligence, 2011.","DOI":"10.1609\/aaai.v25i1.7917"},{"key":"5","unstructured":"[5] I. Cantador, I. Fernandez-Tobias, and S.B.P. Cremonesi, \u201cCross-domain recommender systems.\u201d"},{"key":"6","unstructured":"[6] R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, \u201cNatural language processing (almost) from scratch,\u201d Proc. The Journal of Machine Learning Research, vol.12, pp.2493-2537, 2011."},{"key":"7","unstructured":"[7] I. Fernandez-Tobias1, I. Cantador, M. Kaminskas, and F. Ricci, \u201cCross-domain recommender systems: A survey of the State of the Art,\u201d Spanish Conference on Information Retrieval, 2012."},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] J. F\u00fcrnkranz, E. H\u00fcllermeier, E.L. Mencia, and K. Brinker, \u201cMultilabel classification via calibrated label ranking,\u201d Proc. Machine Learning, vol.73, no.2, pp.133-153, 2008.","DOI":"10.1007\/s10994-008-5064-8"},{"key":"9","unstructured":"[9] S. Gao, H. Luo, D. Chen, S. Li, P. Gallinari, and J. Guo, \u201cCross-domain recommendation via cluster-level latent factor model,\u201d Proc. In Machine Learning and Knowledge Discovery in Databases, vol.8189, pp.161-176, 2013."},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] S. Givon and V. Lavrenko, \u201cPredicting social-tags for cold start book recommendations,\u201d Proc. 3rd ACM Conference on Recommender Systems, pp.333-336, 2009.","DOI":"10.1145\/1639714.1639781"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] Z. Gantner, S. Rendle, C. Freudenthaler, L. Schmidt-Thieme, \u201cMyMediaLite: A Free Recommender System Library,\u201d Proc. In the fifth ACM conference on Recommender systems, pp.305-308, 2011.","DOI":"10.1145\/2043932.2043989"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] M. Kaminskas, I. Fernandez-Tobias, and I. Cantador, \u201cOntology-based identification of music for places,\u201d Proc. 13th International Conference on Information and Communication Technologies in Tourism, pp.436-447, 2013.","DOI":"10.1007\/978-3-642-36309-2_37"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] Y. Koren, \u201cFactorization meets the neighborhood: A multifaceted collaborative filtering model,\u201d Proc. 14th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp.426-434, 2008.","DOI":"10.1145\/1401890.1401944"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] Y. Koren, R.M. Bell, and C. Volinsky, \u201cMatrix factorization techniques for recommender systems,\u201d Proc. IEEE Computer, vol.42, no.8, pp.30-37, 2009.","DOI":"10.1109\/MC.2009.263"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] R. Kumar, B.K. Verma, and S.S. Rastogi, \u201cSocial popularity based svd++ recommender system,\u201d Proc. International Journal of Computer Applications, vol.87, no.14, pp.33-37, 2014.","DOI":"10.5120\/15279-4033"},{"key":"16","unstructured":"[16] B. Li and X. Xue, \u201cCan movies and books collaborate? cross-domain collaborative filtering for sparsity reduction,\u201d Proc. IJCAI, vol.9, pp.2052-2057, 2009."},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] B. Li, Q. Yang, and X. Xue, \u201cTransfer learning for collaborative filtering via a rating-matrix generative model,\u201d Proc. 26th Annual International Conference on Machine Learning, pp.617-624, 2009.","DOI":"10.1145\/1553374.1553454"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] Y. Low, D. Agarwal, and A. Smola, \u201cMultiple domain user personalization,\u201d Proc. 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp.123-131, 2011.","DOI":"10.1145\/2020408.2020434"},{"key":"19","unstructured":"[19] T. Mikolov, W. tau Yih, and G. Zweig, \u201cLinguistic regularities in continuous space word representations,\u201d Proc. HLT-NAACL, pp.746-751, 2013."},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] O. Moreno, B. Shapira, L. Rokach, and G. Shani, \u201cTALMUD: Transfer learning for multiple domains,\u201d Proc. 21st ACM Conference on Information and Knowledge Management, pp.425-434, 2012.","DOI":"10.1145\/2396761.2396817"},{"key":"21","unstructured":"[21] M. Nakatsuji, Y. Fujiwara, A. Tanaka, T. Uchiyama, and T. Ishida, \u201cRecommendations over domain specific user graphs,\u201d Proc. 19th European Conference on Artificial Intelligence, pp.607-612, 2010."},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] M. Nakatsuji, \u201cSemantic Sensitive Simultaneous Tensor Factorization.\u201d International Semantic Web Conference. Springer International Publishing, pp.411-427, 2016.","DOI":"10.1007\/978-3-319-46523-4_25"},{"key":"23","unstructured":"[23] E. Neuhold, \u201cPersonalization and user profiling & recommender systems,\u201d Proc. WIIM Information Management Proseminar, 2003."},{"key":"24","unstructured":"[24] W. Pan, N. Liu, E. Xiang, and Q. Yang, \u201cTransfer learning to predict missing ratings via heterogeneous user feedbacks,\u201d Proc. 22nd International Joint Conference on Artificial Intelligence, pp.2318-2323, 2011."},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] W. Pan, E. Xiang, and Q. Yang, \u201cTransfer learning in collaborative filtering with uncertain ratings,\u201d Proc. 26th AAAI Conference on Artificial Intelligence, pp.662-668, 2012.","DOI":"10.1609\/aaai.v26i1.8197"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] M. Pazzani and D. Billsus, \u201cLearning and revising user profiles: The identification of interesting web sites,\u201d Proc. Machine Learning, vol.27, pp.313-331, 1997.","DOI":"10.1023\/A:1007369909943"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] S. Ren and S. Gao, \u201cImproving cross-domain recommendation through probabilistic cluster-level latent factor model,\u201d Proc. 29th AAAI Conference on Artificial Intelligence. 2015.","DOI":"10.1609\/aaai.v29i1.9706"},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, \u201cItem-based collaborative filtering recommendation algorithms,\u201d Proc. WWW, pp.285-295, May 2001.","DOI":"10.1145\/371920.372071"},{"key":"29","unstructured":"[29] R. Socher, D. Chen, C.D. Manning, and A.Y. Ng, \u201cReasoning with neural tensor networks for knowledge base completion,\u201d Proc. NIPS, pp.926-934, 2013."},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] X. Su and T.M. Khoshgoftaar, \u201cA survey of collaborative filtering techniques,\u201d Proc. Artificial Intellegence, 2009.","DOI":"10.1155\/2009\/421425"},{"key":"31","unstructured":"[31] Y. Zhao, S. Gao, P. Gallinari, and J. Guo, \u201cKnowledge base completion by learning pairwise-interaction differentiated embeddings,\u201d Proc. Data Mining and Knowledge Discovery, vol.29, no.5, pp.1486-1504, 2015."}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E100.D\/6\/E100.D_2016EDP7421\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T21:08:16Z","timestamp":1692824896000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E100.D\/6\/E100.D_2016EDP7421\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":31,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2017]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2016edp7421","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017]]}}}