{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T10:23:00Z","timestamp":1771064580839,"version":"3.50.1"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030474256","type":"print"},{"value":"9783030474263","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-47426-3_5","type":"book-chapter","created":{"date-parts":[[2020,5,8]],"date-time":"2020-05-08T06:02:49Z","timestamp":1588917769000},"page":"53-64","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Relation Embedding for Personalised Translation-Based POI Recommendation"],"prefix":"10.1007","author":[{"given":"Xianjing","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1237-1664","authenticated-orcid":false,"given":"Flora D.","family":"Salim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3137-9653","authenticated-orcid":false,"given":"Yongli","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Piotr","family":"Koniusz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,5,6]]},"reference":[{"issue":"9","key":"5_CR1","first-page":"1616","volume":"30","author":"H Cai","year":"2018","unstructured":"Cai, H., Zheng, V.W., Chang, K.C.C.: A comprehensive survey of graph embedding: problems, techniques, and applications. TKDE 30(9), 1616\u20131637 (2018)","journal-title":"TKDE"},{"key":"5_CR2","unstructured":"Cheng, Z., Caverlee, J., Lee, K., Sui, D.Z.: Exploring millions of footprints in location sharing services. In: AAAI (2011)"},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: SIGKDD, pp. 1082\u20131090 (2011)","DOI":"10.1145\/2020408.2020579"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Grover, A., Leskovec, J.: Node2Vec: scalable feature learning for networks. In: KDD, pp. 855\u2013864 (2016)","DOI":"10.1145\/2939672.2939754"},{"key":"5_CR5","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: NIPS, pp. 1024\u20131034. Curran Associates Inc., (2017)"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"He, R., Kang, W.C., McAuley, J.: Translation-based recommendation. In: RecSys, pp. 161\u2013169 (2017)","DOI":"10.1145\/3109859.3109882"},{"key":"5_CR7","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Li, X., Cong, G., Li, X.L., Pham, T.A.N., Krishnaswamy, S.: Rank-geofm: a ranking based geographical factorization method for point of interest recommendation. In: SIGIR, pp. 433\u2013442 (2015)","DOI":"10.1145\/2766462.2767722"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Lian, D., Zhao, C., Xie, X., Sun, G., Chen, E., Rui, Y.: Geomf: joint geographical modeling and matrix factorization for point-of-interest recommendation. In: KDD, pp. 831\u2013840 (2014)","DOI":"10.1145\/2623330.2623638"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Lichman, M., Smyth, P.: Modeling human location data with mixtures of kernel densities. In: SIGKDD, pp. 35\u201344 (2014)","DOI":"10.1145\/2623330.2623681"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Lin, Y., Liu, Z., Luan, H., Sun, M., Rao, S., Liu, S.: Modeling relation paths for representation learning of knowledge bases. In: EMNLP, pp. 705\u2013714 (2015)","DOI":"10.18653\/v1\/D15-1082"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: AAAI, pp. 2181\u20132187 (2015)","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Liu, X., Liu, Y., Aberer, K., Miao, C.: Personalized point-of-interest recommendation by mining users\u2019 preference transition. In: CIKM, pp. 733\u2013738 (2013)","DOI":"10.1145\/2505515.2505639"},{"key":"5_CR14","unstructured":"Mairal, J., Koniusz, P., Harchaoui, Z., Schmid, C.: Convolutional kernel networks. In: NIPS, pp. 2627\u20132635. Curran Associates Inc., (2014)"},{"key":"5_CR15","unstructured":"Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: NIPS, pp. 1257\u20131264 (2008)"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Palumbo, E., Rizzo, G., Troncy, R.: Entity2rec: learning user-item relatedness from knowledge graphs for top-n item recommendation. In: RecSys (2017)","DOI":"10.1145\/3109859.3109889"},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: KDD, pp. 701\u2013710 (2014)","DOI":"10.1145\/2623330.2623732"},{"issue":"2","key":"5_CR18","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1145\/3295499","volume":"37","author":"T Qian","year":"2019","unstructured":"Qian, T., Liu, B., Nguyen, Q.V.H., Yin, H.: Spatiotemporal representation learning for translation-based poi recommendation. TOIS 37(2), 18 (2019)","journal-title":"TOIS"},{"issue":"2","key":"5_CR19","first-page":"357","volume":"31","author":"C Shi","year":"2018","unstructured":"Shi, C., Hu, B., Zhao, W.X., Philip, S.Y.: Heterogeneous information network embedding for recommendation. TKDE 31(2), 357\u2013370 (2018)","journal-title":"TKDE"},{"key":"5_CR20","unstructured":"Sun, K., Koniusz, P., Wang, Z.: Fisher-bures adversary graph convolutional networks. In: UAI (2019)"},{"issue":"12","key":"5_CR21","first-page":"2724","volume":"29","author":"Q Wang","year":"2017","unstructured":"Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. TKDE 29(12), 2724\u20132743 (2017)","journal-title":"TKDE"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Wang, W., Yin, H., Chen, L., Sun, Y., Sadiq, S., Zhou, X.: Geo-sage: a geographical sparse additive generative model for spatial item recommendation. In: KDD (2015)","DOI":"10.1145\/2783258.2783335"},{"issue":"6","key":"5_CR23","first-page":"61","volume":"9","author":"W Wang","year":"2018","unstructured":"Wang, W., Yin, H., Du, X., Nguyen, Q.V.H., Zhou, X.: Tpm: a temporal personalized model for spatial item recommendation. ACM TIST 9(6), 61 (2018)","journal-title":"ACM TIST"},{"key":"5_CR24","doi-asserted-by":"crossref","unstructured":"Xie, M., Yin, H., Wang, H., Xu, F., Chen, W., Wang, S.: Learning graph-based poi embedding for location-based recommendation. In: CIKM, pp. 15\u201324 (2016)","DOI":"10.1145\/2983323.2983711"},{"issue":"11","key":"5_CR25","first-page":"2537","volume":"29","author":"H Yin","year":"2017","unstructured":"Yin, H., Wang, W., Wang, H., Chen, L., Zhou, X.: Spatial-aware hierarchical collaborative deep learning for poi recommendation. TKDE 29(11), 2537\u20132551 (2017)","journal-title":"TKDE"},{"issue":"10","key":"5_CR26","first-page":"2566","volume":"28","author":"H Yin","year":"2016","unstructured":"Yin, H., Zhou, X., Cui, B., Wang, H., Zheng, K., Nguyen, Q.V.H.: Adapting to user interest drift for poi recommendation. TKDE 28(10), 2566\u20132581 (2016)","journal-title":"TKDE"},{"key":"5_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma, W.Y.: Collaborative knowledge base embedding for recommender systems. In: SIGKDD, pp. 353\u2013362 (2016)","DOI":"10.1145\/2939672.2939673"},{"key":"5_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, J.D., Chow, C.Y.: Geosoca: exploiting geographical, social and categorical correlations for point-of-interest recommendations. In: SIGIR, pp. 443\u2013452 (2015)","DOI":"10.1145\/2766462.2767711"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-47426-3_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,23]],"date-time":"2022-10-23T08:34:31Z","timestamp":1666514071000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-47426-3_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030474256","9783030474263"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-47426-3_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"6 May 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 May 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 May 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pakdd2020.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT System","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"628","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"135","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6-8","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}