{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T22:50:37Z","timestamp":1761864637395,"version":"3.40.3"},"publisher-location":"Cham","reference-count":51,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031560262"},{"type":"electronic","value":"9783031560279"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-56027-9_1","type":"book-chapter","created":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T07:02:49Z","timestamp":1710831769000},"page":"3-20","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Self Contrastive Learning for\u00a0Session-Based Recommendation"],"prefix":"10.1007","author":[{"given":"Zhengxiang","family":"Shi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aldo","family":"Lipani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,20]]},"reference":[{"key":"1_CR1","doi-asserted-by":"publisher","unstructured":"Bae, S., Kim, S., Ko, J., Lee, G., Noh, S., Yun, S.Y.: Self-contrastive learning: single-viewed supervised contrastive framework using sub-network. Proc. AAAI Conf. Artif. Intell. 37(1), 197\u2013205 (2023). https:\/\/doi.org\/10.1609\/aaai.v37i1.25091. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/25091","DOI":"10.1609\/aaai.v37i1.25091"},{"key":"1_CR2","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, Conference Track Proceedings, San Diego, CA, USA, 7\u20139 May 2015 (2015). http:\/\/arxiv.org\/abs\/1409.0473"},{"key":"1_CR3","doi-asserted-by":"publisher","unstructured":"Brost, B., Mehrotra, R., Jehan, T.: The music streaming sessions dataset. In: Liu, L., et al. (eds.) The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, 13\u201317 May 2019, pp. 2594\u20132600. ACM, USA (2019). https:\/\/doi.org\/10.1145\/3308558.3313641","DOI":"10.1145\/3308558.3313641"},{"key":"1_CR4","unstructured":"Chung, J., G\u00fcl\u00e7ehre, \u00c7., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. CoRR abs\/1412.3555 (2014). http:\/\/arxiv.org\/abs\/1412.3555"},{"key":"1_CR5","doi-asserted-by":"publisher","unstructured":"Fu, X., Lipani, A.: Priming and actions: an analysis in conversational search systems. In: Association for Computing Machinery, SIGIR 2023, July 2023. https:\/\/doi.org\/10.1145\/3539618.3592041","DOI":"10.1145\/3539618.3592041"},{"key":"1_CR6","doi-asserted-by":"publisher","unstructured":"Fu, X., Yilmaz, E., Lipani, A.: Evaluating the Cranfield paradigm for conversational search systems. In: Proceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval, ICTIR 2022, pp. 275\u2013280. Association for Computing Machinery, New York (2022). https:\/\/doi.org\/10.1145\/3539813.3545126","DOI":"10.1145\/3539813.3545126"},{"key":"1_CR7","doi-asserted-by":"publisher","unstructured":"Gao, T., Yao, X., Chen, D.: SimCSE: simple contrastive learning of sentence embeddings. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Online and Punta Cana, Dominican Republic, pp. 6894\u20136910. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.552. https:\/\/aclanthology.org\/2021.emnlp-main.552","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"1_CR8","doi-asserted-by":"publisher","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020, pp. 9726\u20139735 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00975","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"1_CR9","unstructured":"Hendriksen, M., Kuiper, E., Nauts, P., Schelter, S., de Rijke, M.: Analyzing and predicting purchase intent in e-commerce: anonymous vs. identified customers. arXiv preprint arXiv:2012.08777 (2020)"},{"key":"1_CR10","unstructured":"Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, Conference Track Proceedings, 2\u20134 May 2016 (2016). http:\/\/arxiv.org\/abs\/1511.06939"},{"issue":"8","key":"1_CR11","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"issue":"3","key":"1_CR12","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1007\/s11257-017-9194-1","volume":"27","author":"D Jannach","year":"2017","unstructured":"Jannach, D., Ludewig, M., Lerche, L.: Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts. User Model. User-Adap. Inter. 27(3), 351\u2013392 (2017)","journal-title":"User Model. User-Adap. Inter."},{"key":"1_CR13","doi-asserted-by":"publisher","unstructured":"Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: Lim, E., et al. (eds.) Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, Singapore, 06\u201310 November 2017, pp. 1419\u20131428. ACM, Singapore (2017). https:\/\/doi.org\/10.1145\/3132847.3132926","DOI":"10.1145\/3132847.3132926"},{"key":"1_CR14","doi-asserted-by":"publisher","unstructured":"Liu, Q., Zeng, Y., Mokhosi, R., Zhang, H.: STAMP: short-term attention\/memory priority model for session-based recommendation. In: Guo, Y., Farooq, F. (eds.) Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, 19\u201323 August 2018, pp. 1831\u20131839. ACM, UK (2018). https:\/\/doi.org\/10.1145\/3219819.3219950","DOI":"10.1145\/3219819.3219950"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Liu, Z., Chen, Y., Li, J., Yu, P.S., McAuley, J., Xiong, C.: Contrastive self-supervised sequential recommendation with robust augmentation. arXiv preprint arXiv:2108.06479 (2021)","DOI":"10.1145\/3485447.3512090"},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Ma, J., Zhou, C., Yang, H., Cui, P., Wang, X., Zhu, W.: Disentangled self-supervision in sequential recommenders. In: Gupta, R., Liu, Y., Tang, J., Prakash, B.A. (eds.) The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2020, Virtual Event, CA, USA, 23\u201327 August 2020, pp. 483\u2013491. ACM (2020). https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403091","DOI":"10.1145\/3394486.3403091"},{"key":"1_CR17","doi-asserted-by":"publisher","unstructured":"Nie, P., et al.: MIC: model-agnostic integrated cross-channel recommender. In: Proceedings of the 31st ACM International Conference on Information and Knowledge Management, CIKM 2022, pp. 3400\u20133409. Association for Computing Machinery, New York (2022). https:\/\/doi.org\/10.1145\/3511808.3557081","DOI":"10.1145\/3511808.3557081"},{"key":"1_CR18","unstructured":"van den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)"},{"key":"1_CR19","doi-asserted-by":"publisher","unstructured":"Qiu, R., Huang, Z., Yin, H., Wang, Z.: Contrastive learning for representation degeneration problem in sequential recommendation. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, WSDM 2022, pp. 813\u2013823. Association for Computing Machinery, New York (2022). https:\/\/doi.org\/10.1145\/3488560.3498433","DOI":"10.1145\/3488560.3498433"},{"key":"1_CR20","doi-asserted-by":"publisher","unstructured":"Qiu, R., Li, J., Huang, Z., Yin, H.: Rethinking the item order in session-based recommendation with graph neural networks. In: Zhu, W., et al. (eds.) Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, 3\u20137 November 2019, pp. 579\u2013588. ACM, Beijing (2019). https:\/\/doi.org\/10.1145\/3357384.3358010","DOI":"10.1145\/3357384.3358010"},{"key":"1_CR21","doi-asserted-by":"publisher","unstructured":"Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized Markov chains for next-basket recommendation. In: Rappa, M., Jones, P., Freire, J., Chakrabarti, S. (eds.) Proceedings of the 19th International Conference on World Wide Web, WWW 2010, Raleigh, North Carolina, USA, 26\u201330 April 2010, pp. 811\u2013820. ACM, USA (2010). https:\/\/doi.org\/10.1145\/1772690.1772773","DOI":"10.1145\/1772690.1772773"},{"key":"1_CR22","first-page":"1265","volume":"6","author":"G Shani","year":"2005","unstructured":"Shani, G., Heckerman, D., Brafman, R.I.: An MDP-based recommender system. J. Mach. Learn. Res. 6, 1265\u20131295 (2005)","journal-title":"J. Mach. Learn. Res."},{"key":"1_CR23","doi-asserted-by":"publisher","unstructured":"Shi, Z., Feng, Y., Lipani, A.: Learning to execute actions or ask clarification questions. In: Findings of the Association for Computational Linguistics, NAACL 2022, Seattle, United States, pp. 2060\u20132070. Association for Computational Linguistics (2022). https:\/\/doi.org\/10.18653\/v1\/2022.findings-naacl.158. https:\/\/aclanthology.org\/2022.findings-naacl.158","DOI":"10.18653\/v1\/2022.findings-naacl.158"},{"key":"1_CR24","unstructured":"Shi, Z., Lipani, A.: Don\u2019t stop pretraining? Make prompt-based fine-tuning powerful learner. In: Thirty-seventh Conference on Neural Information Processing Systems. NeurIPS (2023). https:\/\/openreview.net\/forum?id=s7xWeJQACI"},{"key":"1_CR25","doi-asserted-by":"crossref","unstructured":"Shi, Z., Ni, P., Wang, M., Kim, T.E., Lipani, A.: Attention-based ingredient parser. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium (2022). https:\/\/doi.org\/10.14428\/esann\/2022.ES2022-10","DOI":"10.14428\/esann\/2022.ES2022-10"},{"key":"1_CR26","unstructured":"Shi, Z., Ramos, J., Kim, T.E., Wang, X., Rahmani, H.A., Lipani, A.: When and what to ask through world states and text instructions: IGLU NLP challenge solution. In: Advances in Neural Information Processing Systems (NeurIPS), IGLU Workshop (2023). https:\/\/nips.cc\/virtual\/2022\/66405"},{"key":"1_CR27","doi-asserted-by":"publisher","unstructured":"Shi, Z., Tonolini, F., Aletras, N., Yilmaz, E., Kazai, G., Jiao, Y.: Rethinking semi-supervised learning with language models. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Findings of the Association for Computational Linguistics, ACL 2023, Toronto, Canada, July 2023, pp. 5614\u20135634. Association for Computational Linguistics (2023). https:\/\/doi.org\/10.18653\/v1\/2023.findings-acl.347. https:\/\/aclanthology.org\/2023.findings-acl.347","DOI":"10.18653\/v1\/2023.findings-acl.347"},{"key":"1_CR28","doi-asserted-by":"publisher","unstructured":"Shi, Z., Zhang, Q., Lipani, A.: StepGame: a new benchmark for robust multi-hop spatial reasoning in texts. In: Proceedings of the AAAI Conference on Artificial Intelligence, June 2022, vol. 36, pp. 11321\u201311329 (2022). https:\/\/doi.org\/10.1609\/aaai.v36i10.21383. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/21383","DOI":"10.1609\/aaai.v36i10.21383"},{"key":"1_CR29","doi-asserted-by":"publisher","unstructured":"Song, W., Xiao, Z., Wang, Y., Charlin, L., Zhang, M., Tang, J.: Session-based social recommendation via dynamic graph attention networks. In: Culpepper, J.S., Moffat, A., Bennett, P.N., Lerman, K. (eds.) Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM 2019, Melbourne, VIC, Australia, 11\u201315 February 2019, pp. 555\u2013563. ACM (2019). https:\/\/doi.org\/10.1145\/3289600.3290989","DOI":"10.1145\/3289600.3290989"},{"key":"1_CR30","doi-asserted-by":"publisher","unstructured":"Sun, F., et al.: BERT4Rec: sequential recommendation with bidirectional encoder representations from transformer. In: Zhu, W., et al. (eds.) Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, 3\u20137 November 2019, pp. 1441\u20131450. ACM, Beijing (2019). https:\/\/doi.org\/10.1145\/3357384.3357895","DOI":"10.1145\/3357384.3357895"},{"key":"1_CR31","doi-asserted-by":"publisher","unstructured":"Wang, L., Lim, E.P., Liu, Z., Zhao, T.: Explanation guided contrastive learning for sequential recommendation. In: Proceedings of the 31st ACM International Conference on Information and Knowledge Management, CIKM 2022, pp. 2017\u20132027. Association for Computing Machinery, New York (2022). https:\/\/doi.org\/10.1145\/3511808.3557317","DOI":"10.1145\/3511808.3557317"},{"key":"1_CR32","unstructured":"Wang, T., Isola, P.: Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, Virtual Event, 13\u201318 July 2020, vol. 119, pp. 9929\u20139939. Proceedings of Machine Learning Research (PMLR) (2020). http:\/\/proceedings.mlr.press\/v119\/wang20k.html"},{"key":"1_CR33","doi-asserted-by":"publisher","unstructured":"Wang, W., et al.: Beyond clicks: modeling multi-relational item graph for session-based target behavior prediction. In: Huang, Y., King, I., Liu, T., van Steen, M. (eds.) The Web Conference 2020, WWW 2020, Taipei, Taiwan, 20\u201324 April 2020, pp. 3056\u20133062. ACM\/IW3C2, Taiwan (2020). https:\/\/doi.org\/10.1145\/3366423.3380077","DOI":"10.1145\/3366423.3380077"},{"key":"1_CR34","doi-asserted-by":"publisher","unstructured":"Wang, Z., Wei, W., Cong, G., Li, X., Mao, X., Qiu, M.: Global context enhanced graph neural networks for session-based recommendation. In: Huang, J., et al. (eds.) Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020, Virtual Event, China, 25\u201330 July 2020, pp. 169\u2013178. ACM (2020). https:\/\/doi.org\/10.1145\/3397271.3401142","DOI":"10.1145\/3397271.3401142"},{"key":"1_CR35","doi-asserted-by":"publisher","unstructured":"Wei, Y., et al.: Contrastive learning for cold-start recommendation. In: Proceedings of the 29th ACM International Conference on Multimedia, MM 2021, pp. 5382\u20135390. Association for Computing Machinery, New York (2021). https:\/\/doi.org\/10.1145\/3474085.3475665","DOI":"10.1145\/3474085.3475665"},{"key":"1_CR36","doi-asserted-by":"publisher","unstructured":"Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, 27 January\u20131 February 2019, pp. 346\u2013353. AAAI Press (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.3301346","DOI":"10.1609\/aaai.v33i01.3301346"},{"key":"1_CR37","doi-asserted-by":"publisher","unstructured":"Xia, X., Yin, H., Yu, J., Shao, Y., Cui, L.: Self-supervised graph co-training for session-based recommendation. In: Proceedings of the 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, pp. 2180\u20132190. ACM (2021). https:\/\/doi.org\/10.1145\/3459637.3482388. https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3482388","DOI":"10.1145\/3459637.3482388"},{"key":"1_CR38","doi-asserted-by":"publisher","unstructured":"Xia, X., Yin, H., Yu, J., Wang, Q., Cui, L., Zhang, X.: Self-supervised hypergraph convolutional networks for session-based recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, vol. 35, pp. 4503\u20134511. AAAI (2021). https:\/\/doi.org\/10.1609\/aaai.v35i5.16578. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/16578","DOI":"10.1609\/aaai.v35i5.16578"},{"key":"1_CR39","doi-asserted-by":"crossref","unstructured":"Xie, R., Qiu, Z., Zhang, B., Lin, L.: Multi-granularity item-based contrastive recommendation. arXiv preprint arXiv:2207.01387 (2022)","DOI":"10.1007\/978-3-031-30672-3_27"},{"key":"1_CR40","unstructured":"Xie, X., Sun, F., Liu, Z., Gao, J., Ding, B., Cui, B.: Contrastive pre-training for sequential recommendation. arXiv preprint arXiv:2010.14395 (2020)"},{"key":"1_CR41","doi-asserted-by":"crossref","unstructured":"Xie, X., et al.: Contrastive learning for sequential recommendation. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE), virtual, pp. 1259\u20131273. IEEE (2022). https:\/\/ieeexplore.ieee.org\/abstract\/document\/9835621","DOI":"10.1109\/ICDE53745.2022.00099"},{"key":"1_CR42","doi-asserted-by":"publisher","unstructured":"Xu, C., et al.: Graph contextualized self-attention network for session-based recommendation. In: Kraus, S. (ed.) Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, 10\u201316 August 2019, pp. 3940\u20133946. ijcai.org (2019). https:\/\/doi.org\/10.24963\/ijcai.2019\/547","DOI":"10.24963\/ijcai.2019\/547"},{"key":"1_CR43","unstructured":"Yao, T., et al.: Self-supervised learning for deep models in recommendations. arXiv e-prints pp. arXiv-2007 (2020)"},{"key":"1_CR44","series-title":"SpringerBriefs in Computer Science","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-0748-4","volume-title":"Spatio-Temporal Recommendation in Social Media","author":"H Yin","year":"2016","unstructured":"Yin, H., Cui, B.: Spatio-Temporal Recommendation in Social Media. SCS, Springer, Singapore (2016). https:\/\/doi.org\/10.1007\/978-981-10-0748-4"},{"key":"1_CR45","doi-asserted-by":"crossref","unstructured":"Yu, J., Yin, H., Gao, M., Xia, X., Zhang, X., Hung, N.Q.V.: Socially-aware self-supervised tri-training for recommendation. arXiv preprint arXiv:2106.03569 (2021)","DOI":"10.1145\/3447548.3467340"},{"key":"1_CR46","doi-asserted-by":"crossref","unstructured":"Yu, J., Yin, H., Li, J., Wang, Q., Hung, N.Q.V., Zhang, X.: Self-supervised multi-channel hypergraph convolutional network for social recommendation. arXiv preprint arXiv:2101.06448 (2021)","DOI":"10.1145\/3442381.3449844"},{"key":"1_CR47","doi-asserted-by":"publisher","unstructured":"Yu, J., et al.: Are graph augmentations necessary? Simple graph contrastive learning for recommendation. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2022, pp. 1294\u20131303. Association for Computing Machinery, New York (2022). https:\/\/doi.org\/10.1145\/3477495.3531937","DOI":"10.1145\/3477495.3531937"},{"key":"1_CR48","unstructured":"Zhang, Y., et al.: Sequential click prediction for sponsored search with recurrent neural networks. In: Brodley, C.E., Stone, P. (eds.) Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 27\u201331 July 2014, Qu\u00e9bec City, Qu\u00e9bec, Canada, pp. 1369\u20131375. AAAI Press (2014). http:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI14\/paper\/view\/8529"},{"key":"1_CR49","doi-asserted-by":"publisher","unstructured":"Zhou, K., et al.: S3-Rec: self-supervised learning for sequential recommendation with mutual information maximization. In: d\u2019Aquin, M., Dietze, S., Hauff, C., Curry, E., Cudr\u00e9-Mauroux, P. (eds.) The 29th ACM International Conference on Information and Knowledge Management, CIKM 2020, Virtual Event, Ireland, 19\u201323 October 2020, pp. 1893\u20131902. ACM (2020). https:\/\/doi.org\/10.1145\/3340531.3411954","DOI":"10.1145\/3340531.3411954"},{"key":"1_CR50","doi-asserted-by":"publisher","unstructured":"Zhou, X., Sun, A., Liu, Y., Zhang, J., Miao, C.: SelfCF: a simple framework for self-supervised collaborative filtering. ACM Trans. Recomm. Syst. 1, 1\u201325 (2023). https:\/\/doi.org\/10.1145\/3591469","DOI":"10.1145\/3591469"},{"key":"1_CR51","unstructured":"Zimdars, A., Chickering, D.M., Meek, C.: Using temporal data for making recommendations. arXiv preprint arXiv:1301.2320 (2013)"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-56027-9_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T07:03:11Z","timestamp":1710831791000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-56027-9_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031560262","9783031560279"],"references-count":51,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-56027-9_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"20 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 March 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 March 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.ecir2024.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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"578","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":"110","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":"69","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":"19% - 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":"4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31 (Tracks: Workshop, Tutorial, Industry, Doctoral Consortium)","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)"}}]}}