{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T22:05:30Z","timestamp":1743026730741,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030922726"},{"type":"electronic","value":"9783030922733"}],"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\/978-3-030-92273-3_27","type":"book-chapter","created":{"date-parts":[[2021,12,4]],"date-time":"2021-12-04T21:34:27Z","timestamp":1638653667000},"page":"323-337","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["An Efficient Manifold Density Estimator for\u00a0All Recommendation Systems"],"prefix":"10.1007","author":[{"given":"Jacek","family":"D\u0105browski","sequence":"first","affiliation":[]},{"given":"Barbara","family":"Rychalska","sequence":"additional","affiliation":[]},{"given":"Micha\u0142","family":"Daniluk","sequence":"additional","affiliation":[]},{"given":"Dominika","family":"Basaj","sequence":"additional","affiliation":[]},{"given":"Konrad","family":"Go\u0142uchowski","sequence":"additional","affiliation":[]},{"given":"Piotr","family":"B\u0105bel","sequence":"additional","affiliation":[]},{"given":"Andrzej","family":"Micha\u0142owski","sequence":"additional","affiliation":[]},{"given":"Adam","family":"Jakubowski","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,5]]},"reference":[{"key":"27_CR1","unstructured":"Backurs, A., Indyk, P., Wagner, T.: Space and time efficient kernel density estimation in high dimensions. In: NeurIPS (2019)"},{"key":"27_CR2","doi-asserted-by":"crossref","unstructured":"Ben-Shimon, D., Tsikinovsky, A., Friedmann, M., Shapira, B., Rokach, L., Hoerle, J.: Recsys challenge 2015 and the yoochoose dataset. In: RecSys (2015)","DOI":"10.1145\/2792838.2798723"},{"key":"27_CR3","unstructured":"Bennett, J., Lanning, S., Netflix, N.: The netflix prize. In: In KDD Cup and Workshop in Conjunction with KDD (2007)"},{"key":"27_CR4","doi-asserted-by":"crossref","unstructured":"Charikar, M., Siminelakis, P.: Hashing-based-estimators for kernel density in high dimensions. In: 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS) (2017)","DOI":"10.1109\/FOCS.2017.99"},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Chen, T., Wong, R.C.W.: Handling information loss of graph neural networks for session-based recommendation (2020)","DOI":"10.1145\/3394486.3403170"},{"key":"27_CR6","doi-asserted-by":"crossref","unstructured":"Coleman, B., Shrivastava, A.: Sub-linear race sketches for approximate kernel density estimation on streaming data, pp. 1739\u20131749, April 2020","DOI":"10.1145\/3366423.3380244"},{"key":"27_CR7","doi-asserted-by":"crossref","unstructured":"Coleman, B., Shrivastava, A., Baraniuk, R.G.: Race: sub-linear memory sketches for approximate near-neighbor search on streaming data (2019)","DOI":"10.1145\/3366423.3380244"},{"key":"27_CR8","doi-asserted-by":"crossref","unstructured":"Cormode, G., Muthukrishnan, S.: An improved data stream summary: the count-min sketch and its applications. In: Farach-Colton, M. (ed.) LATIN 2004: Theoretical Informatics (2004)","DOI":"10.1007\/978-3-540-24698-5_7"},{"key":"27_CR9","doi-asserted-by":"crossref","unstructured":"Dacrema, M.F., Cremonesi, P., Jannach, D.: Are we really making much progress? a worrying analysis of recent neural recommendation approaches. In: RecSys (2019)","DOI":"10.1145\/3298689.3347058"},{"key":"27_CR10","unstructured":"Dognin, P., Melnyk, I., Mroueh, Y., Ross, J., Santos, C.D., Sercu, T.: Wasserstein barycenter model ensembling (2019)"},{"key":"27_CR11","doi-asserted-by":"crossref","unstructured":"Greengard, L., Strain, J.: The fast gauss transform (1991)","DOI":"10.1137\/0912004"},{"key":"27_CR12","doi-asserted-by":"crossref","unstructured":"Hidasi, B., Karatzoglou, A.: Recurrent neural networks with top-k gains for session-based recommendations. In: CIKM (2018)","DOI":"10.1145\/3269206.3271761"},{"key":"27_CR13","unstructured":"Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: Conference Proceedings of the Annual ACM Symposium on Theory of Computing (2000)"},{"key":"27_CR14","unstructured":"Itoh, M., Satoh, H.: Geometric mean of probability measures and geodesics of fisher information metric (2017)"},{"key":"27_CR15","unstructured":"Kamehkhosh, I., Jannach, D., Ludewig, M.: A comparison of frequent pattern techniques and a deep learning method for session-based recommendation. In: RecTemp@RecSys (2017)"},{"key":"27_CR16","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) ICLR (20150"},{"key":"27_CR17","unstructured":"LeCun, Y., Cortes, C.: MNIST handwritten digit database (2010). http:\/\/yann.lecun.com\/exdb\/mnist\/"},{"key":"27_CR18","doi-asserted-by":"crossref","unstructured":"Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017. Association for Computing Machinery, New York (2017)","DOI":"10.1145\/3132847.3132926"},{"key":"27_CR19","doi-asserted-by":"crossref","unstructured":"Liang, D., Krishnan, R.G., Hoffman, M.D., Jebara, T.: Variational autoencoders for collaborative filtering. In: WWW (2018)","DOI":"10.1145\/3178876.3186150"},{"key":"27_CR20","doi-asserted-by":"crossref","unstructured":"Liu, Q., Zeng, Y., Mokhosi, R., Zhang, H.: Stamp: short-term attention\/memory priority model for session-based recommendation. In: KDD (2018)","DOI":"10.1145\/3219819.3219950"},{"issue":"4\u20135","key":"27_CR21","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/s11257-018-9209-6","volume":"28","author":"M Ludewig","year":"2018","unstructured":"Ludewig, M., Jannach, D.: Evaluation of session-based recommendation algorithms. User Model. User-Adap. Inter. 28(4\u20135), 331\u2013390 (2018)","journal-title":"User Model. User-Adap. Inter."},{"key":"27_CR22","doi-asserted-by":"crossref","unstructured":"Ludewig, M., Mauro, N., Latifi, S., Jannach, D.: Performance comparison of neural and non-neural approaches to session-based recommendation. In: RecSys (2019)","DOI":"10.1145\/3298689.3347041"},{"key":"27_CR23","unstructured":"Mi, F., Faltings, B.: Context tree for adaptive session-based recommendation. http:\/\/arxiv.org\/abs\/1806.03733"},{"key":"27_CR24","doi-asserted-by":"crossref","unstructured":"Ning, X., Karypis, G.: Slim: sparse linear methods for top-n recommender systems. In: ICDM (2011)","DOI":"10.1109\/ICDM.2011.134"},{"key":"27_CR25","doi-asserted-by":"crossref","unstructured":"Paudel, B., Christoffel, F., Newell, C., Bernstein, A.: Updatable, accurate, diverse, and scalable recommendations for interactive applications. ACM Trans. Interact. Intell, Syst 7, 1\u201334 (2016)","DOI":"10.1145\/2955101"},{"key":"27_CR26","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP) (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"27_CR27","doi-asserted-by":"crossref","unstructured":"Ruocco, M., Skrede, O.S.L., Langseth, H.: Inter-session modeling for session-based recommendation. In: DLRS (2017)","DOI":"10.1145\/3125486.3125491"},{"key":"27_CR28","doi-asserted-by":"crossref","unstructured":"Rychalska, B., B\u0105bel, P., Go\u0142uchowski, K., Micha\u0142owski, A., Dabrowski, J.: Cleora: a simple, strong and scalable graph embedding scheme. arXiv https:\/\/arxiv.org\/abs\/2102.02302 (2020)","DOI":"10.1007\/978-3-030-92273-3_28"},{"key":"27_CR29","unstructured":"Siminelakis, P., Rong, K., Bailis, P., Charikar, M., Levis, P.: Rehashing kernel evaluation in high dimensions. In: International Conference on Machine Learning (2019)"},{"key":"27_CR30","doi-asserted-by":"crossref","unstructured":"Steck, H.: Embarrassingly shallow autoencoders for sparse data. In: WWW (2019)","DOI":"10.1145\/3308558.3313710"},{"key":"27_CR31","unstructured":"Tallec, C., Ollivier, Y.: Unbiasing truncated backpropagation through time. arXiv preprint arXiv:1705.08209 (2017)"},{"key":"27_CR32","doi-asserted-by":"crossref","unstructured":"Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. In: AAAI (2019)","DOI":"10.1609\/aaai.v33i01.3301346"},{"key":"27_CR33","doi-asserted-by":"crossref","unstructured":"Yu, F., Zhu, Y., Liu, Q., Wu, S., Wang, L., Tan, T.: TAGNN: target attentive graph neural networks for session-based recommendation. association for computing machinery (2020)","DOI":"10.1145\/3397271.3401319"},{"key":"27_CR34","unstructured":"Yuan, F., Karatzoglou, A., Arapakis, I., Jose, J.M., He, X.: A simple but hard-to-beat baseline for session-based recommendations (2018), http:\/\/arxiv.org\/abs\/1808.05163"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-92273-3_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T05:32:16Z","timestamp":1673933536000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-92273-3_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030922726","9783030922733"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-92273-3_27","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":"5 December 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sanur, Bali","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","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":"8 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2021.apnns.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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1093","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":"226","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":"177","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":"2.57","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","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 COVID-19 pandemic the conference was held online.","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)"}}]}}