{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T08:47:05Z","timestamp":1766738825355,"version":"3.37.3"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T00:00:00Z","timestamp":1662422400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T00:00:00Z","timestamp":1662422400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71771177","71871143"],"award-info":[{"award-number":["71771177","71871143"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"DOI":"10.1007\/s00521-022-07756-7","type":"journal-article","created":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T13:02:41Z","timestamp":1662469361000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Multimodal deep collaborative filtering recommendation based on dual attention"],"prefix":"10.1007","author":[{"given":"Pei","family":"Yin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dandan","family":"Ji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongcheng","family":"Gan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinxian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,6]]},"reference":[{"issue":"2","key":"7756_CR1","first-page":"155","volume":"5","author":"M Meyskens","year":"2015","unstructured":"Meyskens M, Bird L (2015) Crowdfunding and value creation. Entrep Res J 5(2):155\u2013166","journal-title":"Entrep Res J"},{"key":"7756_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knosys.2019.06.019","volume":"187","author":"N Nassar","year":"2020","unstructured":"Nassar N, Jafar A, Rahhal Y (2020) A novel deep multi-criteria collaborative filtering model for recommendation system. Knowl-Based Syst 187:1\u20137","journal-title":"Knowl-Based Syst"},{"issue":"3","key":"7756_CR3","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.1109\/TCYB.2018.2795041","volume":"49","author":"M Fu","year":"2019","unstructured":"Fu M, Qu H, Yi Z et al (2019) A novel deep learning-based collaborative filtering model for recommendation system. IEEE Trans Cybern 49(3):1084\u20131096","journal-title":"IEEE Trans Cybern"},{"key":"7756_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/s10257-019-00401-2","author":"Y Chen","year":"2019","unstructured":"Chen Y (2019) Research on personalized recommendation algorithm based on user preference in mobile e-commerce. Inf Syst E-Bus Manage. https:\/\/doi.org\/10.1007\/s10257-019-00401-2","journal-title":"Inf Syst E-Bus Manage"},{"key":"7756_CR5","doi-asserted-by":"crossref","unstructured":"Hu Y, Fan X, Zhang R et al (2015) Context-aware web services recommendation based on user preference expansion. In: APSCC 2015: advances in services computing, pp 108\u2013120","DOI":"10.1007\/978-3-319-26979-5_8"},{"issue":"4","key":"7756_CR6","doi-asserted-by":"publisher","first-page":"11501","DOI":"10.1007\/s10586-017-1414-2","volume":"22","author":"LB Xu","year":"2019","unstructured":"Xu LB, Li XS, Guo Y (2019) Gauss-core extension dependent prediction algorithm for collaborative filtering recommendation. Cluster Comput 22(4):11501\u201311511","journal-title":"Cluster Comput"},{"key":"7756_CR7","first-page":"1","volume":"2021","author":"Z Yuan","year":"2021","unstructured":"Yuan Z, Lee JH, Zhang S (2021) Optimization of the hybrid movie recommendation system based on weighted classification and user collaborative filtering algorithm. Complexity 2021:1\u201313","journal-title":"Complexity"},{"key":"7756_CR8","doi-asserted-by":"publisher","first-page":"3097","DOI":"10.1007\/s00521-021-06573-8","volume":"34","author":"ZW Wu","year":"2022","unstructured":"Wu ZW, Chen CT, Huang SH (2022) Poisoning attacks against knowledge graph-based recommendation systems using deep reinforcement learning. Neural Comput Appl 34:3097\u20133115","journal-title":"Neural Comput Appl"},{"key":"7756_CR9","doi-asserted-by":"crossref","unstructured":"Wang F, Wen Y, Guo T, et al. Collaborative filtering and association rule mining\u2010based market basket recommendation on spark. Concurrency and Computation: Practice and Experience, 2020, 32(7).","DOI":"10.1002\/cpe.5565"},{"key":"7756_CR10","doi-asserted-by":"crossref","unstructured":"Wang H, Wang N, Yeung D-Y (2015) Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1235\u20131244","DOI":"10.1145\/2783258.2783273"},{"key":"7756_CR11","doi-asserted-by":"crossref","unstructured":"Qu Y, Han C, Kan R et al (2016) Product-based neural networks for user response prediction. In: Proceedings of the 16th international conference on data mining, pp 1149\u20131154","DOI":"10.1109\/ICDM.2016.0151"},{"key":"7756_CR12","doi-asserted-by":"crossref","unstructured":"Guo H, Tang R, Ye Y et al (2017) DeepFM: a factorization-machine based neural network for CTR prediction. In: Proceedings of the 26th international joint conference on artificial intelligence","DOI":"10.24963\/ijcai.2017\/239"},{"issue":"14","key":"7756_CR13","doi-asserted-by":"publisher","first-page":"4926","DOI":"10.3390\/app10144926","volume":"10","author":"R Lara-Cabrera","year":"2020","unstructured":"Lara-Cabrera R, Gonz\u00e1lez-prieto \u00c1, Ortega F (2020) Deep matrix factorization approach for collaborative filtering recommender systems. Appl Sci 10(14):4926","journal-title":"Appl Sci"},{"key":"7756_CR14","doi-asserted-by":"crossref","unstructured":"Grbovic M, Cheng H (2018) Real-time personalization using embeddings for search ranking at airbnb. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 311\u2013320","DOI":"10.1145\/3219819.3219885"},{"issue":"8","key":"7756_CR15","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1038\/s42256-020-0218-x","volume":"2","author":"Y Luo","year":"2020","unstructured":"Luo Y, Peng J, Ma J (2020) When causal inference meets deep learning. Nat Mach Intell 2(8):426\u2013427","journal-title":"Nat Mach Intell"},{"key":"7756_CR16","unstructured":"Bahdanau D, Cho KH, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: Proceedings of the 3rd international conference on learning representations"},{"key":"7756_CR17","unstructured":"Liu G, Zhang L, Wu J et al (2021) Recommendation with attribute-aware product networks: a representation learning model. In: Proceedings of the 17th ACM conference"},{"key":"7756_CR18","doi-asserted-by":"crossref","unstructured":"Xiao J, Ye H, He X et al (2017) Attentional factorization machines: learning the weight of feature interactions via attention networks. In: Proceedings of the 26th international joint conference on artificial intelligence, pp 3119\u20133125","DOI":"10.24963\/ijcai.2017\/435"},{"key":"7756_CR19","doi-asserted-by":"crossref","unstructured":"Rendle S (2010) Factorization machines. In: Proceedings of the 10th international conference on data mining, pp 995\u20131000","DOI":"10.1109\/ICDM.2010.127"},{"key":"7756_CR20","doi-asserted-by":"crossref","unstructured":"Zhou G, Zhu X, Song C et al (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","DOI":"10.1145\/3219819.3219823"},{"key":"7756_CR21","doi-asserted-by":"crossref","unstructured":"Zhou G, Mou N, Fan Y et al (2019) Deep interest evolution network for click-through rate prediction. In: Proceedings of the AAAI conference on artificial intelligence, pp 5941\u20135948","DOI":"10.1609\/aaai.v33i01.33015941"},{"key":"7756_CR22","doi-asserted-by":"crossref","unstructured":"Song W, Shi C, Xiao Z et al (2019) AutoInt: automatic feature interaction learning via self-attentive neural networks. Proceedings of the 28th ACM international conference on information and knowledge management, pp 1161\u20131170","DOI":"10.1145\/3357384.3357925"},{"issue":"12","key":"7756_CR23","doi-asserted-by":"publisher","first-page":"2354","DOI":"10.1109\/TKDE.2018.2831682","volume":"30","author":"X He","year":"2018","unstructured":"He X, He Z, Song J et al (2018) NAIS: neural attentive item similarity model for recommendation. IEEE Trans Knowl Data Eng 30(12):2354\u20132366","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"7756_CR24","doi-asserted-by":"publisher","DOI":"10.1108\/INTR-08-2020-0477","author":"J Fan","year":"2021","unstructured":"Fan J, Jiang YC, Liu YZ et al (2021) Interpretable MOOC recommendation: a multi-attention network for personalized learning behavior analysis. Internet Res. https:\/\/doi.org\/10.1108\/INTR-08-2020-0477","journal-title":"Internet Res"},{"key":"7756_CR25","doi-asserted-by":"crossref","unstructured":"Song W, Xiao Z, Wang Y et al (2019) Session-based social recommendation via dynamic graph attention networks. In: Proceedings of the 12th ACM international conference on web search and data mining, pp 555\u2013563","DOI":"10.1145\/3289600.3290989"},{"key":"7756_CR26","doi-asserted-by":"publisher","first-page":"4335","DOI":"10.1007\/s00521-018-03964-2","volume":"32","author":"Y Liu","year":"2020","unstructured":"Liu Y, Tian Z, Sun J et al (2020) Distributed representation learning via node2vec for implicit feedback recommendation. Neural Comput Appl 32:4335\u20134345","journal-title":"Neural Comput Appl"},{"key":"7756_CR27","volume-title":"Banking and finance","author":"H Wang","year":"2020","unstructured":"Wang H, Chen S (2020) A Bipartite graph-based recommender for crowdfunding with sparse data. In: Haron R, Husin MM, Murg M (eds) Banking and finance. IntechOpen, London"},{"issue":"1","key":"7756_CR28","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1287\/mnsc.2020.3930","volume":"68","author":"Y Song","year":"2020","unstructured":"Song Y, Li Z, Sahoo N (2020) Matching returning donors to projects on philanthropic crowdfunding platform. Manage Sci 68(1):355\u2013375","journal-title":"Manage Sci"},{"key":"7756_CR29","doi-asserted-by":"crossref","unstructured":"Rakesh V, Lee W-C, Reddy CK (2016) Probabilistic group recommendation model for crowdfunding domains. In: Proceedings of the ninth ACM international conference on web search and data mining, pp 257\u2013266","DOI":"10.1145\/2835776.2835793"},{"issue":"18","key":"7756_CR30","first-page":"49","volume":"57","author":"R Zeyu","year":"2021","unstructured":"Zeyu R, Zhenchao W, Zunwang K et al (2021) A review of multimodal data fusion. Comput Eng Appl 57(18):49\u201364","journal-title":"Comput Eng Appl"},{"key":"7756_CR31","unstructured":"Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: Proceedings of the 31st international conference on international conference on machine learning, pp 1188\u20131196"},{"key":"7756_CR32","doi-asserted-by":"crossref","unstructured":"He X, Liao L, Zhang H et al (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web, pp 173\u2013182","DOI":"10.1145\/3038912.3052569"},{"key":"7756_CR33","doi-asserted-by":"crossref","unstructured":"Xue H-J, Dai X-Y, Zhang J et al (2017) Deep matrix factorization models for recommender systems. In: Proceedings of the 26th international joint conference on artificial intelligence, pp 3203\u20133209","DOI":"10.24963\/ijcai.2017\/447"},{"key":"7756_CR34","doi-asserted-by":"crossref","unstructured":"Mcmahan HB, Holt G, Sculley D et al (2013) Ad click prediction: a view from the trenches. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1222\u20131230","DOI":"10.1145\/2487575.2488200"},{"key":"7756_CR35","doi-asserted-by":"crossref","unstructured":"He X, Chua T-S (2017) Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, pp 355\u2013364","DOI":"10.1145\/3077136.3080777"},{"key":"7756_CR36","doi-asserted-by":"crossref","unstructured":"Fan Z, Liu Z, Wang Y et al (2022) Sequential recommendation via stochastic self-attention. In: Proceedings of the ACM web conference 2022. Virtual Event, Lyon, France; Association for Computing Machinery, pp 2036\u20132047","DOI":"10.1145\/3485447.3512077"},{"key":"7756_CR37","doi-asserted-by":"crossref","unstructured":"Kang WC, Mcauley J (2018) Self-attentive sequential recommendation. In: 2018 IEEE international conference on data mining (ICDM)","DOI":"10.1109\/ICDM.2018.00035"},{"key":"7756_CR38","first-page":"2579","volume":"9","author":"GE Hinton","year":"2008","unstructured":"Hinton GE (2008) Visualizing High-dimensional data using t-SNE. Vigiliae Christianae 9:2579\u20132605","journal-title":"Vigiliae Christianae"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07756-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07756-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07756-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T13:04:28Z","timestamp":1662469468000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07756-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,6]]},"references-count":38,"alternative-id":["7756"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07756-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2022,9,6]]},"assertion":[{"value":"30 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there is no conflict of interest with any financial organizations regarding the material reported in this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}