{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T08:45:20Z","timestamp":1742978720630,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030757649"},{"type":"electronic","value":"9783030757656"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/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":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-75765-6_44","type":"book-chapter","created":{"date-parts":[[2021,5,7]],"date-time":"2021-05-07T09:08:54Z","timestamp":1620378534000},"page":"549-560","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["UKIRF: An Item Rejection Framework for Improving Negative Items Sampling in One-Class Collaborative Filtering"],"prefix":"10.1007","author":[{"given":"Ant\u00f4nio David","family":"Viniski","sequence":"first","affiliation":[]},{"given":"Jean Paul","family":"Barddal","sequence":"additional","affiliation":[]},{"suffix":"Jr.","given":"Alceu","family":"de Souza Britto","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,8]]},"reference":[{"issue":"1","key":"44_CR1","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/S0306-4573(02)00021-3","volume":"39","author":"AN Aizawa","year":"2003","unstructured":"Aizawa, A.N.: An information-theoretic perspective of tf-idf measures. Inf. Process. Manage. 39(1), 45\u201365 (2003)","journal-title":"Inf. Process. Manage."},{"key":"44_CR2","doi-asserted-by":"crossref","unstructured":"Harper, F.M., Konstan, J.A.: The MovieLens datasets: history and context. TiiS 5(4), 19:1\u201319:19 (2016)","DOI":"10.1145\/2827872"},{"key":"44_CR3","doi-asserted-by":"crossref","unstructured":"He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, WWW 2017, 3\u20137 April 2017, Perth, Australia, pp. 173\u2013182. ACM (2017)","DOI":"10.1145\/3038912.3052569"},{"key":"44_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/978-3-642-41278-3_74","volume-title":"Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2013","author":"B Li","year":"2013","unstructured":"Li, B., Han, L.: Distance weighted cosine similarity measure for text classification. In: Yin, H., Tang, K., Gao, Y., Klawonn, F., Lee, M., Weise, T., Li, B., Yao, X. (eds.) IDEAL 2013. LNCS, vol. 8206, pp. 611\u2013618. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-41278-3_74"},{"key":"44_CR5","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.knosys.2017.02.034","volume":"124","author":"G Li","year":"2017","unstructured":"Li, G., Zhang, Z., Wang, L., Chen, Q., Pan, J.: One-class collaborative filtering based on rating prediction and ranking prediction. Knowl. Based Syst. 124, 46\u201354 (2017)","journal-title":"Knowl. Based Syst."},{"key":"44_CR6","doi-asserted-by":"crossref","unstructured":"Pan, R., et al: One-class collaborative filtering. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), 15\u201319 December 2008, Pisa, Italy, pp. 502\u2013511. IEEE Computer Society (2008)","DOI":"10.1109\/ICDM.2008.16"},{"issue":"4","key":"44_CR7","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/MIS.2016.19","volume":"31","author":"W Pan","year":"2016","unstructured":"Pan, W., Liu, M., Ming, Z.: Transfer learning for heterogeneous one-class collaborative filtering. IEEE Intell. Syst. 31(4), 43\u201349 (2016)","journal-title":"IEEE Intell. Syst."},{"key":"44_CR8","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. CoRR abs\/1205.2618 (2012)"},{"key":"44_CR9","doi-asserted-by":"crossref","unstructured":"Shi, Y., Larson, M.A., Hanjalic, A.: List-wise learning to rank with matrix factorization for collaborative filtering. In: Proceedings of the 2010 ACM Conference on Recommender Systems, RecSys 2010, 26\u201330 September 2010, Barcelona, Spain, pp. 269\u2013272. ACM (2010)","DOI":"10.1145\/1864708.1864764"},{"key":"44_CR10","doi-asserted-by":"crossref","unstructured":"Sidana, S., Laclau, C., Amini, M., Vandelle, G., Bois-Crettez, A.: KASANDR: a large-scale dataset with implicit feedback for recommendation. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 7\u201311 August 2017, Shinjuku, Tokyo, Japan, pp. 1245\u20131248. ACM (2017)","DOI":"10.1145\/3077136.3080713"},{"key":"44_CR11","doi-asserted-by":"crossref","unstructured":"Song, B., Yang, X., Cao, Y., Xu, C.: Neural collaborative ranking. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, 22\u201326 October 2018, Torino, Italy, pp. 1353\u20131362. ACM (2018)","DOI":"10.1145\/3269206.3271715"},{"key":"44_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/978-3-319-08786-3_41","volume-title":"User Modeling, Adaptation, and Personalization","author":"J Vinagre","year":"2014","unstructured":"Vinagre, J., Jorge, A.M., Gama, J.: Fast incremental matrix factorization for recommendation with positive-only feedback. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, G.-J. (eds.) UMAP 2014. LNCS, vol. 8538, pp. 459\u2013470. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-08786-3_41"},{"key":"44_CR13","doi-asserted-by":"crossref","unstructured":"Volkovs, M., Yu, G.W.: Effective latent models for binary feedback in recommender systems. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 9\u201313 August 2015, Santiago, Chile, pp. 313\u2013322. ACM (2015)","DOI":"10.1145\/2766462.2767716"},{"issue":"1\u20132","key":"44_CR14","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.mcm.2010.07.022","volume":"53","author":"J Ye","year":"2011","unstructured":"Ye, J.: Cosine similarity measures for intuitionistic fuzzy sets and their applications. Math. Comput. Model. 53(1\u20132), 91\u201397 (2011)","journal-title":"Math. Comput. Model."},{"key":"44_CR15","doi-asserted-by":"crossref","unstructured":"Yu, H., Bilenko, M., Lin, C.: Selection of negative samples for one-class matrix factorization. In: Proceedings of the 2017 SIAM International Conference on Data Mining, 27\u201329 April 2017, Houston, Texas, USA, pp. 363\u2013371. SIAM (2017)","DOI":"10.1137\/1.9781611974973.41"},{"key":"44_CR16","doi-asserted-by":"crossref","unstructured":"Yuan, Q., Chen, L., Zhao, S.: Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation. In: Proceedings of the 2011 ACM Conference on Recommender Systems, RecSys 2011, 23\u201327 October 2011, Chicago, IL, USA, pp. 245\u2013252. ACM (2011)","DOI":"10.1145\/2043932.2043975"},{"key":"44_CR17","doi-asserted-by":"crossref","unstructured":"Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: a survey and new perspectives. ACM Comput. Surv. 52(1), 5:1\u20135:38 (2019)","DOI":"10.1145\/3285029"},{"key":"44_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, W., Chen, T., Wang, J., Yu, Y.: Optimizing top-n collaborative filtering via dynamic negative item sampling. In: The 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2013, 28 July\u201301 August 2013, Dublin, Ireland, pp. 785\u2013788. ACM (2013)","DOI":"10.1145\/2484028.2484126"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-75765-6_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T10:53:01Z","timestamp":1699008781000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-75765-6_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030757649","9783030757656"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-75765-6_44","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":"8 May 2021","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 May 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 May 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2021.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"673","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":"157","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":"23% - 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","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)"}}]}}