{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:13:28Z","timestamp":1778602408027,"version":"3.51.4"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T00:00:00Z","timestamp":1687824000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T00:00:00Z","timestamp":1687824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The out-of-home (OOH) advertising market has been operated exclusively following the know-how of salespeople. Thus, it is difficult to make scientific decisions and systematically provide various options to advertisers. In this regard, this study develops an OOH advertising recommendation system by analyzing past OOH history data. The OOH advertising allocation problem has the characteristics that the training data are implicit feedback, and only one advertisement can be posted per offline billboard. This study proposes a recommendation system suitable for OOH history data using negative sampling and Deep Interest Network. The proposed recommendation system showed a higher performance than excisting models used for comparison purposes, and the findings of this study present implications for solving similar recommendation problems.<\/jats:p>","DOI":"10.1007\/s11042-023-16083-5","type":"journal-article","created":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T01:02:06Z","timestamp":1687827726000},"page":"11943-11955","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Development of an offline OOH advertising recommendation system using negative sampling and deep interest network"],"prefix":"10.1007","volume":"83","author":[{"given":"Hyun-Woo","family":"Seo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soo-Hyeok","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sang-Gi","family":"Ryu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seung-Kyu","family":"Jo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Su-Phil","family":"Cho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jong-Soo","family":"Sohn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi-Ehyeon","family":"Lim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,27]]},"reference":[{"issue":"8","key":"16083_CR1","first-page":"5375","volume":"34","author":"K Berahmand","year":"2022","unstructured":"Berahmand K, Nasiri E, Forouzandeh S, Li Y (2022) A preference random walk algorithm for link prediction through mutual influence nodes in complex networks. J King Saud Univ-Comput Inf Sci 34(8):5375\u20135387","journal-title":"J King Saud Univ-Comput Inf Sci"},{"issue":"2","key":"16083_CR2","doi-asserted-by":"publisher","first-page":"185","DOI":"10.5391\/JKIIS.2006.16.2.185","volume":"16","author":"YH Choi","year":"2006","unstructured":"Choi YH, Lee SY (2006) Users\u2019 moving patterns analysis for personalized product recommendation in offline shopping malls. J Korean Inst Intell Syst 16(2):185\u2013190. https:\/\/doi.org\/10.5391\/JKIIS.2006.16.2.185","journal-title":"J Korean Inst Intell Syst"},{"key":"16083_CR3","doi-asserted-by":"crossref","unstructured":"Ding, J., Quan, Y., He, X., Li, Y., & Jin, D. (2019) Reinforced Negative Sampling for Recommendation with Exposure Data. Proceeding of IJCAI 2019:2230\u20132236","DOI":"10.24963\/ijcai.2019\/309"},{"issue":"01","key":"16083_CR4","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1142\/S0219622020500522","volume":"20","author":"S Forouzandeh","year":"2021","unstructured":"Forouzandeh S, Berahmand K, Nasiri E, Rostami M (2021) A hotel recommender system for tourists using the Artificial Bee Colony Algorithm and Fuzzy TOPSIS Model: a case study of tripadvisor. Int J Inf Technol Decis Mak 20(01):399\u2013429","journal-title":"Int J Inf Technol Decis Mak"},{"issue":"1","key":"16083_CR5","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1080\/16168658.2021.2019430","volume":"14","author":"S Forouzandeh","year":"2022","unstructured":"Forouzandeh S, Rostami M, Berahmand K (2022) A hybrid method for recommendation systems based on tourism with an evolutionary algorithm and topsis model. Fuzzy Inf Eng 14(1):26\u201350","journal-title":"Fuzzy Inf Eng"},{"key":"16083_CR6","doi-asserted-by":"crossref","unstructured":"Gemmeke JF, Ellis DPW, Freedman D, Jansen A, Lawrence W, Moore RC, Plakal M, Ritter M (2017) Audio set: An ontology and human-labeled dataset for audio events. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): 776\u2013780","DOI":"10.1109\/ICASSP.2017.7952261"},{"key":"16083_CR7","doi-asserted-by":"crossref","unstructured":"Guo H, Tang R, Ye Y, Li Z, He X (2017) DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 . Accessed 23 June 2021","DOI":"10.24963\/ijcai.2017\/239"},{"key":"16083_CR8","doi-asserted-by":"crossref","unstructured":"He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017). Neural Collaborative Filtering. In Proceedings of the 26th International Conference on World Wide Web (WWW '17). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE: 173\u2013182","DOI":"10.1145\/3038912.3052569"},{"key":"16083_CR9","doi-asserted-by":"crossref","unstructured":"He X, Liao L, Zhang H, Nie L, Hu X, Chua TS (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on World Wide Web: 173\u2013182","DOI":"10.1145\/3038912.3052569"},{"key":"16083_CR10","doi-asserted-by":"crossref","unstructured":"He X, Zhang H, Kan MY, Chua TS (2016) Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the 39th international ACM SIGIR conference on Research and Development in Information Retrieval: 549\u2013558","DOI":"10.1145\/2911451.2911489"},{"key":"16083_CR11","doi-asserted-by":"crossref","unstructured":"Hu Y, Koren Y, Volinsky C (2008) Collaborative filtering for implicit feedback datasets. In: vol 2008 Eighth IEEE International Conference on Data Mining. IEEE Publications: 263\u2013272","DOI":"10.1109\/ICDM.2008.22"},{"key":"16083_CR12","unstructured":"Kim NK, Jeong SB (2017) A study on customized brand recommendation system in offline shopping malls using collaboration filtering. Proceeding of 19th Joint Business Adminstration Conference of Korea 2019: 600\u2013619."},{"issue":"4","key":"16083_CR13","first-page":"55","volume":"23","author":"NK Kim","year":"2016","unstructured":"Kim NK, Jeong SB (2016) A Study on Customized Brand Recommendation based on Customer Behavior for off-line Shopping Malls. J Inf Technol Appl Manag 23(4):55\u201370","journal-title":"J Inf Technol Appl Manag"},{"issue":"3","key":"16083_CR14","doi-asserted-by":"publisher","first-page":"211","DOI":"10.26599\/BDMA.2018.9020019","volume":"1","author":"Y Liu","year":"2018","unstructured":"Liu Y, Wang S, Khan MS, He J (2018) A novel deep hybrid recommender system based on auto-encoder with neural collaborative filtering. Big Data Min Anal 1(3):211\u2013221. https:\/\/doi.org\/10.26599\/BDMA.2018.9020019","journal-title":"Big Data Min Anal"},{"key":"16083_CR15","unstructured":"Ministry of Culture, Sports and Tourism (2020) 2019 advertising industry survey. Searched on https:\/\/www.mcst.go.kr\/kor\/s_policy\/dept\/deptView.jsp?pSeq=1300&pDataCD=0417000000&pType=08. Accessed 23 June 2021"},{"key":"16083_CR16","doi-asserted-by":"crossref","unstructured":"Pan R, Zhou Y, Cao B, Liu NN, Lukose R, Scholz M, Yang Q (2008) One-class collaborative filtering. In: vol 2008 Eighth IEEE International Conference on Data Mining. IEEE Publications: 502\u2013511","DOI":"10.1109\/ICDM.2008.16"},{"key":"16083_CR17","first-page":"99","volume":"2020","author":"X Wang","year":"2020","unstructured":"Wang X, Xu Y, He X, Cao Y, Wang M, Chua TS (2020) Reinforced negative sampling over knowledge graph for recommendation. Proc Web Conf 2020:99\u2013109","journal-title":"Proc Web Conf"},{"key":"16083_CR18","doi-asserted-by":"crossref","unstructured":"Xu Z, Chen C, Lukasiewicz T, Miao Y, Meng X (2016) Tag-aware personalized recommendation using a deep-semantic similarity model with negative sampling. In: Proceedings of the 25th ACM international on conference on information and knowledge management. pp 1921\u20131924","DOI":"10.1145\/2983323.2983874"},{"key":"16083_CR19","doi-asserted-by":"crossref","unstructured":"Yih W-T (2009) Learning term-weighting functions for similarity measures. In: Proceedings of the 2009 conference on empirical methods in natural language processing. pp 793\u2013802","DOI":"10.3115\/1699571.1699616"},{"key":"16083_CR20","doi-asserted-by":"crossref","unstructured":"Zhang W, Chen T, Wang J, Yu Y (2013) Optimizing top-n collaborative filtering via dynamic negative item sampling. In: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. pp 785\u2013788","DOI":"10.1145\/2484028.2484126"},{"key":"16083_CR21","doi-asserted-by":"crossref","unstructured":"Zhou G, Zhu X, Song C, Fan Y, Zhu H, Ma X, Yan Y, Jin J, Li H, Gai K (2018) Deep Interest Network for Click-Through Rate Prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18). Association for Computing Machinery, New York, USA. pp 1059\u20131068","DOI":"10.1145\/3219819.3219823"},{"key":"16083_CR22","doi-asserted-by":"publisher","unstructured":"Zhou G, Zhu X, Song C, Fan Y, Zhu H, Ma X,Yan Y, Jin J, Li H, Gai K (2018) Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD international conference on Knowledge Discovery & Data Mining. pp 1059\u20131068. https:\/\/doi.org\/10.1145\/3219819.3219823","DOI":"10.1145\/3219819.3219823"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16083-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16083-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16083-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T09:40:16Z","timestamp":1704879616000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16083-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,27]]},"references-count":22,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["16083"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16083-5","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,27]]},"assertion":[{"value":"25 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and Consent"}}]}}