caa a22 4500 605447292 CHVBK 20210128100131.0 cr unu---uuuuu 210128e20150701xx s 000 0 eng 10.1007/s11042-013-1825-x doi (NATIONALLICENCE)springer-10.1007/s11042-013-1825-x Video recommendation based on multi-modal information and multiple kernel [Elektronische Daten] [Zhan Li, Jin-Ye Peng, Guo-Hua Geng, Xiao-Jiang Chen, Pan-Pan Zheng] Collaborative Filter (CF) algorithms often suffer from data sparsity and item cold start problem, for the user-item matrix is insufficient and extremely sparse especially when new item is added to recommendation system. These two problems also exist in video recommendation process. We propose two methods to solve them by incorporating multimodal information and multiple kernel together. To solve item cold start problem, we learn a user taste hyper-plane by using multiple kernel SVM to represent the user taste, which is further used to predict the recommendation of new added videos. We combine the different user taste hyper-plane similarity and the traditional cosine similarity with a trade-off between them to address the data sparse problem. Experimental results show that our proposed algorithm can alleviate the data sparsity and item cold start problems. Springer Science+Business Media New York, 2014 Video recommendation nationallicence Multi-modal information nationallicence Multiple kernel nationallicence Collaborative filtering nationallicence Li Zhan School of Information Science and Technology, Northwest University, 710069, Xi'an, China aut Peng Jin-Ye School of Information Science and Technology, Northwest University, 710069, Xi'an, China aut Geng Guo-Hua School of Information Science and Technology, Northwest University, 710069, Xi'an, China aut Chen Xiao-Jiang School of Information Science and Technology, Northwest University, 710069, Xi'an, China aut Zheng Pan-Pan School of Information Science and Technology, Northwest University, 710069, Xi'an, China aut Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/13(2015-07-01), 4599-4616 1380-7501 74:13<4599 2015 74 11042 https://doi.org/10.1007/s11042-013-1825-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11042-013-1825-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Li Zhan School of Information Science and Technology, Northwest University, 710069, Xi'an, China aut NATIONALLICENCE 700 1- Peng Jin-Ye School of Information Science and Technology, Northwest University, 710069, Xi'an, China aut NATIONALLICENCE 700 1- Geng Guo-Hua School of Information Science and Technology, Northwest University, 710069, Xi'an, China aut NATIONALLICENCE 700 1- Chen Xiao-Jiang School of Information Science and Technology, Northwest University, 710069, Xi'an, China aut NATIONALLICENCE 700 1- Zheng Pan-Pan School of Information Science and Technology, Northwest University, 710069, Xi'an, China aut NATIONALLICENCE 773 0- Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/13(2015-07-01), 4599-4616 1380-7501 74:13<4599 2015 74 11042