{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T03:47:43Z","timestamp":1775274463367,"version":"3.50.1"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030041663","type":"print"},{"value":"9783030041670","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-030-04167-0_13","type":"book-chapter","created":{"date-parts":[[2018,11,16]],"date-time":"2018-11-16T18:47:46Z","timestamp":1542394066000},"page":"140-151","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Collaborative Filtering Combined with High-Level Feature Generation on Latent Factor Model"],"prefix":"10.1007","author":[{"given":"Xu","family":"Li","sequence":"first","affiliation":[]},{"given":"Xu","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Zheng","family":"Qin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,11,17]]},"reference":[{"key":"13_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","volume-title":"Computer Vision \u2013 ECCV 2014","author":"MD Zeiler","year":"2014","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818\u2013833. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Cheng, H., et al.: Wide & deep learning for recommender systems. In: Conference On Recommender Systems, pp. 7\u201310 (2016)","DOI":"10.1145\/2988450.2988454"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: DeepFM: a factorization-machine based neural network for CTR prediction. In: International Joint Conference on Artificial Intelligence, pp. 1725\u20131731 (2017)","DOI":"10.24963\/ijcai.2017\/239"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Juan, Y., Zhuang, Y., Chin, W.S., Lin, C.J.: Field-aware factorization machines for CTR prediction. In: ACM Conference on Recommender Systems, pp. 43\u201350 (2016)","DOI":"10.1145\/2959100.2959134"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Zhao, Z.D., Shang, M.S.: User-based collaborative-filtering recommendation algorithms on Hadoop. In: International Conference on Knowledge Discovery and Data Mining, pp. 478\u2013481 (2010)","DOI":"10.1109\/WKDD.2010.54"},{"key":"13_CR6","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.knosys.2014.06.003","volume":"67","author":"F Font","year":"2014","unstructured":"Font, F., Serra, J., Serra, X.: Class-based tag recommendation and user-based evaluation in online audio clip sharing. Knowl. Based Syst. 67, 131\u2013142 (2014)","journal-title":"Knowl. Based Syst."},{"issue":"10","key":"13_CR7","first-page":"385","volume":"6","author":"W Tung","year":"2011","unstructured":"Tung, W., Chen, Y.: User-based social ranking service design for tagging search and recommendation. J. Converg. Inf. Technol. 6(10), 385\u2013390 (2011)","journal-title":"J. Converg. Inf. Technol."},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Jamali, M., Ester, M.: TrustWalker : a random walk model for combining trust-based and item-based recommendation. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 397\u2013406 (2009)","DOI":"10.1145\/1557019.1557067"},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: International Conference on World Wide Web, pp. 285\u2013295 (2001)","DOI":"10.1145\/371920.372071"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Li, C., Luo, Z.: A hybrid item-based recommendation algorithm against segment attack in collaborative filtering systems. In: International Conference on Information Management, Innovation Management and Industrial Engineering, pp. 403\u2013406 (2011)","DOI":"10.1109\/ICIII.2011.242"},{"issue":"3","key":"13_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2168752.2168771","volume":"3","author":"S Rendle","year":"2012","unstructured":"Rendle, S.: Factorization machines with libFM. ACM Trans. Intell. Syst. Technol. 3(3), 1\u201322 (2012)","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426\u2013434 (2008)","DOI":"10.1145\/1401890.1401944"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Vozalis, M.G., Margaritis, K.G.: Applying SVD on item-based filtering. In: Proceedings of the International Conference on Intelligent Systems Design and Applications, Isda 2005, pp. 464\u2013469 (2006)","DOI":"10.1109\/ISDA.2005.25"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Bryt, O., Elad, M.: Compression of facial images using the K-SVD algorithm. Academic Press Inc (2008)","DOI":"10.1016\/j.jvcir.2008.03.001"},{"key":"13_CR15","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., Schmidtthieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Uncertainty in Artificial Intelligence, pp. 452\u2013461 (2009)"},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, N., Yeung,D.Y.: Collaborative deep learning for recommender systems. In: Knowledge Discovery and Data Mining, pp. 1235\u20131244 (2015)","DOI":"10.1145\/2783258.2783273"},{"issue":"1","key":"13_CR17","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"13_CR18","first-page":"1431","volume":"9","author":"A Ebadi","year":"2016","unstructured":"Ebadi, A.: An intelligent hybrid multi-criteria hotel recommender system using explicit and implicit feedbacks. Ebadi Ashkan 9, 1431\u20131441 (2016)","journal-title":"Ebadi Ashkan"},{"key":"13_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1007\/978-3-642-28509-7_32","volume-title":"Advances in User Modeling","author":"M Tkal\u010di\u010d","year":"2012","unstructured":"Tkal\u010di\u010d, M., Odi\u0107, A., Ko\u0161ir, A., Tasi\u010d, J.F.: Impact of implicit and explicit affective labeling on a recommender system\u2019s performance. In: Ardissono, L., Kuflik, T. (eds.) UMAP 2011. LNCS, vol. 7138, pp. 342\u2013354. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-28509-7_32"},{"key":"13_CR20","unstructured":"Li, Q., Zheng, X.: Deep collaborative autoencoder for recommender systems: a unified framework for explicit and implicit feedback (2017). arXiv: Learning"},{"key":"13_CR21","unstructured":"Socher, R., Chen, D., Manning, C.D., Ng, A.Y.: Reasoning with neural tensor networks for knowledge base completion. In: International Conference on Neural Information Processing Systems, pp. 926\u2013934 (2013)"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.: Neural collaborative filtering. In: International World Wide Web Conferences, pp. 173\u2013182 (2017)","DOI":"10.1145\/3038912.3052569"},{"key":"13_CR23","doi-asserted-by":"crossref","unstructured":"He, X., Chua, T.S.: Neural factorization machines for sparse predictive analytics. In: The International ACM SIGIR Conference, pp. 355\u2013364 (2017)","DOI":"10.1145\/3077136.3080777"},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"Xiao, J., Ye, H., He, X., Zhang, H., Wu, F., Chua, T.: Attentional factorization machines: learning the weight of feature interactions via attention networks. In: International Joint Conference on Artificial Intelligence, pp. 3119\u20133125 (2017)","DOI":"10.24963\/ijcai.2017\/435"},{"key":"13_CR25","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhang, F., Xie, X., Guo,M.: Dkn: deep knowledge-aware network for news recommendation. In: International World Wide Web Conferences, pp. 1835\u20131844 (2018)","DOI":"10.1145\/3178876.3186175"},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"Yu, W., Zhang, H., He, X., Chen, X., Xiong, L., Qin, Z.: Aesthetic-based clothing recommendation. In: World Wide Web Conference, pp. 649\u2013658 (2018)","DOI":"10.1145\/3178876.3186146"},{"key":"13_CR27","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Telepath: understanding users from a human vision perspective in large-scale recommender systems. In: National Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.11243"},{"key":"13_CR28","doi-asserted-by":"crossref","unstructured":"Lu, Y., Dong, R., Smyth, B.: Coevolutionary recommendation model: mutual learning between ratings and reviews. In: World Wide Web Conference, pp. 773\u2013782 (2018)","DOI":"10.1145\/3178876.3186158"},{"key":"13_CR29","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Ding, Y., Zhu, L., Kankanhalli, M.S.: Aspect-aware latent factor model: rating prediction with ratings and reviews. In: International World Wide Web Conferences, pp. 639\u2013648 (2018)","DOI":"10.1145\/3178876.3186145"},{"key":"13_CR30","first-page":"275","volume-title":"Advances in Intelligent Systems and Computing","author":"Xiaojiao Yao","year":"2017","unstructured":"Yao, X., Tan, B., Hu, C., Li, W., Xu, Z., Zhang, Z.: Recommend algorithm combined user-user neighborhood approach with latent factor model. In: International Conference on Mechatronics and Intelligent Robotics, pp. 275\u2013280 (2017)"},{"key":"13_CR31","first-page":"044","volume":"4","author":"Z He","year":"2016","unstructured":"He, Z.: Personalized recommendation based on latent factor model and trust of users. Comput. Knowl. Technol. 4, 044 (2016)","journal-title":"Comput. Knowl. Technol."}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-04167-0_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T02:53:10Z","timestamp":1775271190000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-04167-0_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030041663","9783030041670"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-04167-0_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"17 November 2018","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":"Siem Reap","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cambodia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 December 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 December 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conference.cs.cityu.edu.hk\/iconip\/","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":"575","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":"401","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":"70% - 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":"4","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)"}}]}}