{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T05:27:25Z","timestamp":1744954045911,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030731991"},{"type":"electronic","value":"9783030732004"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","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":[[2021]]},"DOI":"10.1007\/978-3-030-73200-4_6","type":"book-chapter","created":{"date-parts":[[2021,4,6]],"date-time":"2021-04-06T18:02:46Z","timestamp":1617732166000},"page":"85-99","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Semi-supervised Factorization Machines for Review-Aware Recommendation"],"prefix":"10.1007","author":[{"given":"Junheng","family":"Huang","sequence":"first","affiliation":[]},{"given":"Fangyuan","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,6]]},"reference":[{"issue":"6","key":"6_CR1","doi-asserted-by":"publisher","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 T. Knowl. Data Eng. 17(6), 734\u2013749 (2005)","journal-title":"IEEE T. Knowl. Data Eng."},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Bao, Y., Fang, H., Zhang, J.: TopicMF: simultaneously exploiting ratings and reviews for recommendation. In: AAAI, pp. 2\u20138 (2014)","DOI":"10.1609\/aaai.v28i1.8715"},{"key":"6_CR3","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993\u20131022 (2003)","journal-title":"J. Mach. Learn. Res."},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: COLT, pp. 92\u2013100 (1998)","DOI":"10.1145\/279943.279962"},{"key":"6_CR5","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.knosys.2013.03.012","volume":"46","author":"J Bobadilla","year":"2013","unstructured":"Bobadilla, J., Ortega, F., Hernando, A., Gutierrez, A.: Recommender systems survey. Knowl.-Based Syst. 46, 109\u2013132 (2013)","journal-title":"Knowl.-Based Syst."},{"issue":"1","key":"6_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1921591.1921593","volume":"5","author":"F Cacheda","year":"2011","unstructured":"Cacheda, F., Formoso, V.: Comparison of collaborative filtering algorithms: limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Trans. Web 5(1), 1\u201333 (2011)","journal-title":"ACM Trans. Web"},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Chen, C., Zhang, M., Liu, Y., Ma, S.: Neural attentional rating regression with review-level explanations. In: WWW, pp. 1583\u20131592 (2018)","DOI":"10.1145\/3178876.3186070"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. In: EMNLP, pp. 1746\u20131751 (2014)","DOI":"10.3115\/v1\/D14-1181"},{"key":"6_CR9","doi-asserted-by":"crossref","unstructured":"Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: SIGKDD, pp. 426\u2013434 (2008)","DOI":"10.1145\/1401890.1401944"},{"key":"6_CR10","unstructured":"Koren, Y.: The Bellkor solution to the Netflix grand prize. Technical report, Netflix prize documentation (2009)"},{"issue":"8","key":"6_CR11","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2009.263","volume":"42","author":"Y Koren","year":"2009","unstructured":"Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30\u201337 (2009)","journal-title":"Computer"},{"issue":"1","key":"6_CR12","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1109\/TPAMI.2014.2299812","volume":"37","author":"Y Li","year":"2015","unstructured":"Li, Y., Zhou, Z.: Towards making unlabeled data never hurt. IEEE Trans. Pattern Anal. 37(1), 175\u2013188 (2015)","journal-title":"IEEE Trans. Pattern Anal."},{"issue":"5","key":"6_CR13","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1007\/s10994-016-5599-z","volume":"106","author":"C Liu","year":"2017","unstructured":"Liu, C., Jin, T., Hoi, S.C.H., Zhao, P., Sun, J.: Collaborative topic regression for online recommender systems: an online and Bayesian approach. Mach. Learn. 106(5), 651\u2013670 (2017). https:\/\/doi.org\/10.1007\/s10994-016-5599-z","journal-title":"Mach. Learn."},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"Liu, H., He, X., Feng, F., Nie, L., Liu, R., Zhang, H.: Discrete factorization machines for fast feature-based recommendation. In: IJCAI, pp. 3449\u20133455 (2018)","DOI":"10.24963\/ijcai.2018\/479"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Liu, H., et al.: NRPA: neural recommendation with personalized attention. In: SIGIR, pp. 1233\u20131236 (2019)","DOI":"10.1145\/3331184.3331371"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"McAuley, J.J., Leskovec, J.: Hidden factors and hidden topics: understanding rating dimensions with review text. In: RecSys, pp. 165\u2013172 (2013)","DOI":"10.1145\/2507157.2507163"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Ren, Y., Li, G., Zhang, J., Zhou, W.: The efficient imputation method for neighborhood-based collaborative filtering. In: CIKM, pp. 684\u2013693 (2012)","DOI":"10.1145\/2396761.2396849"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Rendle, S.: Factorization machines. In: ICDM, pp. 995\u20131000 (2010)","DOI":"10.1109\/ICDM.2010.127"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Sachdeva, N., McAuley, J.: How useful are reviews for recommendation? A critical review and potential improvements. In: SIGIR, pp. 1845\u20131848 (2020)","DOI":"10.1145\/3397271.3401281"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Seo, S., Huang, J., Yang, H., Liu, Y.: Interpretable convolutional neural networks with dual local and global attention for review rating prediction. In: RecSys, pp. 297\u2013305 (2017)","DOI":"10.1145\/3109859.3109890"},{"issue":"1","key":"6_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2556270","volume":"47","author":"Y Shi","year":"2014","unstructured":"Shi, Y., Larson, M., Hanjalic, A.: Collaborative filtering beyond the user-item matrix: a survey of the state of the art and future challenges. ACM Comput. Surv. 47(1), 1\u201345 (2014)","journal-title":"ACM Comput. Surv."},{"issue":"5","key":"6_CR22","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1109\/MIS.2020.3016944","volume":"35","author":"J Wu","year":"2020","unstructured":"Wu, J., Luo, F., Zhang, Y., Wang, H.: Semi-discrete matrix factorization. IEEE Intell. Syst. 35(5), 73\u201383 (2020)","journal-title":"IEEE Intell. Syst."},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, H., Shen, F., Liu, W., He, X., Luan, H., Chua, T.S.: Discrete collaborative filtering. In: SIGIR, pp. 325\u2013334 (2016)","DOI":"10.1145\/2911451.2911502"},{"key":"6_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, J., Pu, P.: A recursive prediction algorithm for collaborative filtering recommender systems. In: RecSys, pp. 57\u201364 (2007)","DOI":"10.1145\/1297231.1297241"},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, M., Tang, J., Zhang, X., Xue, X.: Addressing cold start in recommender systems: a semi-supervised co-training algorithm. In: SIGKDD, pp. 73\u201382 (2014)","DOI":"10.1145\/2600428.2609599"},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Zheng, L., Noroozi, V., Yu, P.S.: Joint deep modeling of users and items using reviews for recommendation. In: WSDM, pp. 425\u2013434 (2017)","DOI":"10.1145\/3018661.3018665"},{"key":"6_CR27","unstructured":"Zhou, Z.H., Li, M.: Semi-supervised regression with co-training. In: IJCAI, pp. 908\u2013913 (2005)"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-73200-4_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T14:41:43Z","timestamp":1671806503000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-73200-4_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030731991","9783030732004"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-73200-4_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"6 April 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taipei","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taiwan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 April 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/dm.iis.sinica.edu.tw\/DASFAA2021\/index.html","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","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"490","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":"98","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":"33","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":"20% - 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":"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":"7","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":"Due to the Corona pandemic this event was held virtually.","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)"}}]}}