{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T19:48:10Z","timestamp":1777492090142,"version":"3.51.4"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030881122","type":"print"},{"value":"9783030881139","type":"electronic"}],"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\/978-3-030-88113-9_2","type":"book-chapter","created":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T19:04:24Z","timestamp":1632942264000},"page":"15-29","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Social Recommendation for Social Networks Using Deep Learning Approach: A Systematic Review"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3751-0448","authenticated-orcid":false,"given":"Muhammad","family":"Alrashidi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9746-8459","authenticated-orcid":false,"given":"Ali","family":"Selamat","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7580-1804","authenticated-orcid":false,"given":"Roliana","family":"Ibrahim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5992-2574","authenticated-orcid":false,"given":"Ondrej","family":"Krejcar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,27]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Ricci, F., Rokach, L., Shapira, B.: Recommender Systems Handbook (2011)","DOI":"10.1007\/978-0-387-85820-3"},{"key":"2_CR2","unstructured":"McKinsey & Company: How Retailers can keep up with Consumers, https:\/\/www.mckinsey.com\/industries\/retail\/our-insights\/how-retailers-can-keep-up-with-consumers"},{"issue":"2","key":"2_CR3","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1007\/s10462-019-09684-w","volume":"53","author":"J Shokeen","year":"2019","unstructured":"Shokeen, J., Rana, C.: A study on features of social recommender systems. Artif. Intell. Rev. 53(2), 965\u2013988 (2019). https:\/\/doi.org\/10.1007\/s10462-019-09684-w","journal-title":"Artif. Intell. Rev."},{"issue":"3","key":"2_CR4","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1007\/s10844-019-00578-5","volume":"54","author":"J Shokeen","year":"2019","unstructured":"Shokeen, J., Rana, C.: Social recommender systems: techniques, domains, metrics, datasets and future scope. J. Intell Inf. Syst. 54(3), 633\u2013667 (2019). https:\/\/doi.org\/10.1007\/s10844-019-00578-5","journal-title":"J. Intell Inf. Syst."},{"key":"2_CR5","doi-asserted-by":"publisher","unstructured":"Da\u2019u, A., Salim, N.: Recommendation system based on deep learning methods: a systematic review and new directions. Artif. Intell. Rev. 53, 2709\u20132748 (2020). https:\/\/doi.org\/10.1007\/s10462-019-09744-1","DOI":"10.1007\/s10462-019-09744-1"},{"issue":"1","key":"2_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-018-9654-y","volume":"52","author":"Z Batmaz","year":"2018","unstructured":"Batmaz, Z., Yurekli, A., Bilge, A., Kaleli, C.: A review on deep learning for recommender systems: challenges and remedies. Artif. Intell. Rev. 52(1), 1\u201337 (2018). https:\/\/doi.org\/10.1007\/s10462-018-9654-y","journal-title":"Artif. Intell. Rev."},{"key":"2_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3285029","volume":"52","author":"S Zhang","year":"2019","unstructured":"Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: a survey and new perspectives. ACM Comput. Surv. 52, 1\u201335 (2019). https:\/\/doi.org\/10.1145\/3285029","journal-title":"ACM Comput. Surv."},{"key":"2_CR8","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.infsof.2008.09.009","volume":"51","author":"B Kitchenham","year":"2009","unstructured":"Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., Linkman, S.: Systematic literature reviews in software engineering \u2013 a systematic literature review. Inf. Softw. Technol. 51, 7\u201315 (2009). https:\/\/doi.org\/10.1016\/j.infsof.2008.09.009","journal-title":"Inf. Softw. Technol."},{"key":"2_CR9","doi-asserted-by":"publisher","first-page":"e1000097","DOI":"10.1371\/journal.pmed.1000097","volume":"6","author":"D Moher","year":"2009","unstructured":"Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G.: Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 6, e1000097 (2009). https:\/\/doi.org\/10.1371\/journal.pmed.1000097","journal-title":"PLoS Med."},{"key":"2_CR10","unstructured":"Review, A.S.: Applied sciences recommender system based on temporal models, pp. 1\u201327 (2020)"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Del Carpio, A.F., Angarita, L.B.: Trends in software engineering processes using deep learning: a systematic literature review. In: 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pp. 445\u2013454. IEEE (2020)","DOI":"10.1109\/SEAA51224.2020.00077"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Zhou, X., Jin, Y., Zhang, H., Li, S., Huang, X.: A map of threats to validity of systematic literature reviews in software engineering. In: 2016 23rd Asia-Pacific Software Engineering Conference (APSEC), pp. 153\u2013160. IEEE (2016)","DOI":"10.1109\/APSEC.2016.031"},{"key":"2_CR13","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1109\/TNSE.2020.3044035","volume":"8","author":"L Wan","year":"2021","unstructured":"Wan, L., Xia, F., Kong, X., Hsu, C.-H., Huang, R., Ma, J.: Deep matrix factorization for trust-aware recommendation in social networks. IEEE Trans. Netw. Sci. Eng. 8, 511\u2013528 (2021). https:\/\/doi.org\/10.1109\/TNSE.2020.3044035","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"issue":"5","key":"2_CR14","doi-asserted-by":"publisher","first-page":"1607","DOI":"10.1007\/s00521-020-05085-1","volume":"33","author":"H Tahmasebi","year":"2020","unstructured":"Tahmasebi, H., Ravanmehr, R., Mohamadrezaei, R.: Social movie recommender system based on deep autoencoder network using Twitter data. Neural Comput. Appl. 33(5), 1607\u20131623 (2020). https:\/\/doi.org\/10.1007\/s00521-020-05085-1","journal-title":"Neural Comput. Appl."},{"issue":"4","key":"2_CR15","doi-asserted-by":"publisher","first-page":"2259","DOI":"10.1007\/s11280-020-00793-z","volume":"23","author":"Y Pan","year":"2020","unstructured":"Pan, Y., He, F., Yu, H.: Learning social representations with deep autoencoder for recommender system. World Wide Web 23(4), 2259\u20132279 (2020). https:\/\/doi.org\/10.1007\/s11280-020-00793-z","journal-title":"World Wide Web"},{"key":"2_CR16","doi-asserted-by":"publisher","first-page":"114191","DOI":"10.1016\/j.eswa.2020.114191","volume":"167","author":"J Gao","year":"2021","unstructured":"Gao, J., Zhang, C., Xu, Y., Luo, M., Niu, Z.: Hybrid microblog recommendation with heterogeneous features using deep neural network. Expert Syst. Appl. 167, 114191 (2021). https:\/\/doi.org\/10.1016\/j.eswa.2020.114191","journal-title":"Expert Syst. Appl."},{"key":"2_CR17","doi-asserted-by":"publisher","first-page":"2129","DOI":"10.1109\/TKDE.2019.2915523","volume":"32","author":"S Pramanik","year":"2020","unstructured":"Pramanik, S., Haldar, R., Kumar, A., Pathak, S., Mitra, B.: Deep learning driven venue recommender for event-based social networks. IEEE Trans. Knowl. Data Eng. 32, 2129\u20132143 (2020). https:\/\/doi.org\/10.1109\/TKDE.2019.2915523","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"2_CR18","doi-asserted-by":"publisher","first-page":"2762","DOI":"10.1109\/TMM.2019.2912124","volume":"21","author":"J Zhang","year":"2019","unstructured":"Zhang, J., Yang, Y., Zhuo, L., Tian, Q., Liang, X.: Personalized recommendation of social images by constructing a user interest tree with deep features and tag trees. IEEE Trans. Multimed. 21, 2762\u20132775 (2019). https:\/\/doi.org\/10.1109\/TMM.2019.2912124","journal-title":"IEEE Trans. Multimed."},{"issue":"5","key":"2_CR19","doi-asserted-by":"publisher","first-page":"1937","DOI":"10.1007\/s10489-018-1359-z","volume":"49","author":"CC Nisha","year":"2019","unstructured":"Nisha, C.C., Mohan, A.: A social recommender system using deep architecture and network embedding. Appl. Intell. 49(5), 1937\u20131953 (2019). https:\/\/doi.org\/10.1007\/s10489-018-1359-z","journal-title":"Appl. Intell."},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Zheng, L., et al.: MARS: memory attention-aware recommender system. In: 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 11\u201320. IEEE (2019)","DOI":"10.1109\/DSAA.2019.00015"},{"key":"2_CR21","doi-asserted-by":"publisher","first-page":"13189","DOI":"10.1109\/ACCESS.2019.2892289","volume":"7","author":"R Zhang","year":"2019","unstructured":"Zhang, R., Mao, Y.: Movie recommendation via markovian factorization of matrix processes. IEEE Access. 7, 13189\u201313199 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2892289","journal-title":"IEEE Access."},{"issue":"2","key":"2_CR22","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s10660-019-09377-0","volume":"20","author":"S Shamshoddin","year":"2019","unstructured":"Shamshoddin, S., Khader, J., Gani, S.: Predicting consumer preferences in electronic market based on IoT and Social Networks using deep learning based collaborative filtering techniques. Electron. Commer. Res. 20(2), 241\u2013258 (2019). https:\/\/doi.org\/10.1007\/s10660-019-09377-0","journal-title":"Electron. Commer. Res."},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Garg, D., Gupta, P., Malhotra, P., Vig, L., Shroff, G.: Sequence and time aware neighborhood for session-based recommendations. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1069\u20131072. ACM, New York (2019)","DOI":"10.1145\/3331184.3331322"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Chen, H., Li, J.: Adversarial tensor factorization for context-aware recommendation. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp. 363\u2013367. ACM, New York (2019)","DOI":"10.1145\/3298689.3346987"},{"issue":"10","key":"2_CR25","doi-asserted-by":"publisher","first-page":"1851","DOI":"10.1007\/s10994-018-05777-9","volume":"108","author":"L Zhang","year":"2019","unstructured":"Zhang, L., Liu, P., Gulla, J.A.: Dynamic attention-integrated neural network for session-based news recommendation. Mach. Learn. 108(10), 1851\u20131875 (2019). https:\/\/doi.org\/10.1007\/s10994-018-05777-9","journal-title":"Mach. Learn."},{"key":"2_CR26","doi-asserted-by":"crossref","unstructured":"Song, W., Xiao, Z., Wang, Y., Charlin, L., Zhang, M., Tang, J.: Session-based social recommendation via dynamic graph attention networks. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 555\u2013563. ACM, New York (2019)","DOI":"10.1145\/3289600.3290989"},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"Wu, X., Shi, B., Dong, Y., Huang, C., Chawla, N. V.: Neural tensor factorization for temporal interaction learning. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 537\u2013545. ACM, New York (2019)","DOI":"10.1145\/3289600.3290998"},{"key":"2_CR28","doi-asserted-by":"crossref","unstructured":"Qu, Z., Li, B., Wang, X., Yin, S., Zheng, S.: An efficient recommendation framework on social media platforms based on deep learning. In: 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 599\u2013602. IEEE (2018)","DOI":"10.1109\/BigComp.2018.00104"},{"key":"2_CR29","doi-asserted-by":"crossref","unstructured":"Lu, Y., Dong, R., Smyth, B.: Coevolutionary recommendation model. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW 2018, pp. 773\u2013782. ACM Press, New York (2018)","DOI":"10.1145\/3178876.3186158"},{"issue":"4-5","key":"2_CR30","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). https:\/\/doi.org\/10.1007\/s11257-018-9209-6","journal-title":"User Model. User-Adap. Inter."},{"key":"2_CR31","doi-asserted-by":"crossref","unstructured":"Neammanee, T., Maneeroj, S.: Time-aware recommendation based on user preference driven. In: 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), pp. 26\u201331. IEEE (2018)","DOI":"10.1109\/COMPSAC.2018.10198"},{"key":"2_CR32","doi-asserted-by":"crossref","unstructured":"Niu, W., Caverlee, J., Lu, H.: Neural personalized ranking for image recommendation. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 423\u2013431. ACM, New York (2018)","DOI":"10.1145\/3159652.3159728"},{"key":"2_CR33","doi-asserted-by":"crossref","unstructured":"Liang, D., Krishnan, R.G., Hoffman, M.D., Jebara, T.: Variational autoencoders for collaborative filtering. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW 2018, pp. 689\u2013698. ACM Press, New York (2018)","DOI":"10.1145\/3178876.3186150"},{"key":"2_CR34","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.eswa.2016.09.040","volume":"69","author":"J Wei","year":"2017","unstructured":"Wei, J., He, J., Chen, K., Zhou, Y., Tang, Z.: Collaborative filtering and deep learning based recommendation system for cold start items. Expert Syst. Appl. 69, 29\u201339 (2017). https:\/\/doi.org\/10.1016\/j.eswa.2016.09.040","journal-title":"Expert Syst. Appl."},{"key":"2_CR35","doi-asserted-by":"publisher","first-page":"1164","DOI":"10.1109\/TNNLS.2016.2514368","volume":"28","author":"S Deng","year":"2017","unstructured":"Deng, S., Huang, L., Xu, G., Wu, X., Wu, Z.: On Deep learning for trust-aware recommendations in social networks. IEEE Trans. Neural Netw. Learn. Syst. 28, 1164\u20131177 (2017). https:\/\/doi.org\/10.1109\/TNNLS.2016.2514368","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"2_CR36","doi-asserted-by":"crossref","unstructured":"Dang, Q.-V., Ignat, C.-L.: dTrust: a simple deep learning approach for social recommendation. In: 2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC), pp. 209\u2013218. IEEE (2017)","DOI":"10.1109\/CIC.2017.00036"},{"key":"2_CR37","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1007\/978-3-319-57454-7_15","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"HTH Nguyen","year":"2017","unstructured":"Nguyen, H.T.H., Wistuba, M., Grabocka, J., Drumond, L.R., Schmidt-Thieme, L.: Personalized deep learning for tag recommendation. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10234, pp. 186\u2013197. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-57454-7_15"},{"key":"2_CR38","doi-asserted-by":"crossref","unstructured":"Zheng, L., Noroozi, V., Yu, P.S.: Joint deep modeling of users and items using reviews for recommendation. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 425\u2013434. ACM, New York (2017)","DOI":"10.1145\/3018661.3018665"},{"key":"2_CR39","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: Dynamic attention deep model for article recommendation by learning human editors\u2019 demonstration. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2051\u20132059. ACM, New York (2017)","DOI":"10.1145\/3097983.3098096"},{"key":"2_CR40","doi-asserted-by":"crossref","unstructured":"Cao, S., Yang, N., Liu, Z.: Online news recommender based on stacked auto-encoder. In: 2017 IEEE\/ACIS 16th International Conference on Computer and Information Science (ICIS), pp. 721\u2013726. IEEE (2017)","DOI":"10.1109\/ICIS.2017.7960088"},{"key":"2_CR41","doi-asserted-by":"crossref","unstructured":"Hidasi, B., Quadrana, M., Karatzoglou, A., Tikk, D.: Parallel recurrent neural network architectures for feature-rich session-based recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 241\u2013248. ACM, New York (2016)","DOI":"10.1145\/2959100.2959167"},{"key":"2_CR42","doi-asserted-by":"crossref","unstructured":"Tan, J., Wan, X., Xiao, J.: A neural network approach to quote recommendation in writings. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 65\u201374. ACM, New York (2016)","DOI":"10.1145\/2983323.2983788"},{"key":"2_CR43","doi-asserted-by":"crossref","unstructured":"Lee, H., Ahn, Y., Lee, H., Ha, S., Lee, S.: Quote recommendation in dialogue using deep neural network. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 957\u2013960. ACM, New York (2016)","DOI":"10.1145\/2911451.2914734"},{"key":"2_CR44","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/978-3-319-27674-8_34","volume-title":"MultiMedia Modeling","author":"J Zhou","year":"2016","unstructured":"Zhou, J., Albatal, R., Gurrin, C.: Applying visual user interest profiles for recommendation and personalisation. In: Tian, Q., Sebe, N., Qi, G.-J., Huet, B., Hong, R., Liu, X. (eds.) MMM 2016. LNCS, vol. 9517, pp. 361\u2013366. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-27674-8_34"},{"key":"2_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-319-29659-3_1","volume-title":"Recommender Systems","author":"CC Aggarwal","year":"2016","unstructured":"Aggarwal, C.C.: An introduction to recommender systems. In: Recommender Systems, pp. 1\u201328. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-29659-3_1"}],"container-title":["Communications in Computer and Information Science","Advances in Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-88113-9_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T19:09:58Z","timestamp":1632942598000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-88113-9_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030881122","9783030881139"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-88113-9_2","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"27 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Collective Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rhodos","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"29 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccci2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccci.pwr.edu.pl\/2021\/","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":"231","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":"58","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":"25% - 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":"3","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)"}}]}}