Computer Science ›› 2020, Vol. 47 ›› Issue (4): 103-107.doi: 10.11896/jsjkx.190700177

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Video Recommendation Algorithm for Multidimensional Feature Analysis and Filtering

ZHAO Nan, PI Wen-chao, XU Chang-qiao   

  1. Institute of Network Technology,Beijing University of Posts and Telecommunications,Beijing 100876,China
  • Received:2019-07-25 Online:2020-04-15 Published:2020-04-15
  • Contact: XU Chang-qiao,born in 1977,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include Mobile Internet,multimedia communications,cloud computing and big data,content distribution and transmission.
  • About author:ZHAO Nan,born in 1996,postgraduate.His main research interests include multimedia communication,cloud computing and big data

Abstract: In recent years,short video apps such as TikTok,Kwai,and WeiShi have achieved great success,and the number of videos taken by users and uploaded to the APP platform has skyrocketed.In this environment of information overload,mining and recommending videos of interest to users has become a problem faced by video publishing platforms.Therefore,it is particularly important to design efficient video recommendation algorithms for these platforms.Aiming at the problem of high sparseness and huge scale of datasets in the field of media big data mining and recommendation,a video recommendation algorithm for multidimensional feature analysis and filtering is proposed.First,feature extraction is performed on videos from multiple dimensions such as user behavior and video tags.Then,similarity analysis is performed to calculate the video similarity by weighting to obtain similar video candidate sets,the similar video candidate sets are filtered,and then several videos selected by ranking the highest rated videos are recommended to users.Finally,based on the MovieLens public data set,the video recommendation algorithm proposed in this paper is implemented by using python3 programming language.A large number of experiments on the data set show that compared with the traditional collaborative filtering algorithm,the video recommendation algorithm for multidimensional feature analysis and filtering proposed in the paper improves the accuracy of the recommendation results by 6%,the recall rate by 4%,and the coverage rate by 18%.The experimental data fully demonstrates that considering the similarity between videos from multiple dimensions,combined with large-scale matrix factorization technology,the problems of high sparseness and huge data volume of the data set are alleviated to some extent,thereby effectively improving the recommendation results accuracy,recall,and coverage.

Key words: Video recommendation, Feature extraction, Similarity analysis, Collaborative filtering, Sparsity

CLC Number: 

  • TP399
[1]ZHENG L S,YANG S Q,HE J,et al.An optimized collaborative filtering recommendation algorithm[C]//2016 2nd International Conference on Cloud Computing and Internet of Things (CCIOT).Dalian,IEEE,2016:89-92.
[2]ZHAN M F,LI L,HUANG Q M,et al.Cross-media retrieval with semantics clustering and enhancement[C]//2017 IEEE International Conference on Multimedia and Expo (ICME).Hong Kong,China,IEEE,2017:1398-1403.
[3]BRADLEY K,SMYTH B.Improving recommendation diversity [C]// Proceedings of the Twelfth Irish Conference on Artificial Intelligence and Cognitive Science.Irish:AICS,2001:85-94.
[4]SU L Y,CHEN X B.Improvement of user-based collaborative filtering algorithms[J].Computer Engineering & Software,2017(4):135-140.
[5]HU Y,PENG Q,HU X,et al.Time Aware and Data Sparsity Tolerant Web Service Recommendation Based on Improved Collaborative Filtering[J].IEEE Transactions on Services Computing,2015,8(5):782-794.
[6]WANG X M,ZHANG X M,WU Y T,et al.Collaborative filtering recommendation algorithm based on heuristic clustering model and category similarity [J].Journal of Electronic Science,2016,44 (7):1708-1713.
[7]KUMAR Y,SHARMA A,KHAUND A,et al.IceBreaker:Solving Cold Start Problem for Video Recommendation Engines[C]//2018 IEEE International Symposium on Multimedia (ISM).IEEE,2018:217-222.
[8]PATRA B K,LAUNONEN R,OLLIKAINEN V,et al.Exploiting bhattacharyya similarity measure to diminish user cold-start problem in sparse data[M]//Discovery Science.Cham:Springer,2014:252-263.
[9]HUANG C G,YIN J,WANG J,et al.Uncertain neighbors collaborative filtering recommendation algorithm[J].Chinese Journal of Computers,2010,33(8):1369-1377.
[10]HE Y,YANG S,JIAO C,et al.A Hybrid Collaborative Filtering Recommendation Algorithm for Solving the Data Sparsity[C]// 2011 International Symposium on Computer Science and Society.2011:118-121.
[11]WANG P,YE H W.A Personalized Recommendation Algorithm Combining Slope One Scheme and User Based Collaborative Filtering [C]// 2009 International Conference on Industrial and Information Systems.2009:152-154.
[12]CACHEDA F,FORMOSO V.Comparison of collaborative filtering algorithms:Limitations of current techniques and proposals for scalable,high -performance recommender systems [J].ACM Transactions on the Web,2011,5(1):1-33.
[13]DESHPANDE M,KARYPIS G.Item-based top-N recommendation algorithms[J].ACM Transactionon Information Systems,2004,22(1):143-177.
[14]WANG P,QIAN Q,SHANG Z,et al.An recommendation algorithm based on weighted Slope one algorithm and user-based collaborative filtering [C]// 2016 Chinese Control and Decision Conference (CCDC).2016:2431-2434.
[15]ZARZOUR H,AL-SHARIF Z,AL-AYYOUB M,et al.A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques [C]// 2018 9th International Conference on Information and Communication Systems (ICICS).2018:102-106.
[16]ARORA S,GOEL S.Improving the Accuracy of Recommender Systems Through Annealing[J].Lecture Notes in Networks and Systems,2017,1(1):295-304.
[17]KOREN Y,BELL R,VOLINSKY C.Matrix Factorization Techniques for Recommender Systems[J].Computer,2009,42(8):30-37.
[18]SHEUGH L,ALIZADEH S H.A note on pearson correlation coefficient as a metric of similarity in recommender system [C]// 2015 AI & Robotics (IRANOPEN).2015:1-6.
[1] LIU Yang, JIN Zhong. Fine-grained Image Recognition Method Combining with Non-local and Multi-region Attention Mechanism [J]. Computer Science, 2021, 48(1): 197-203.
[2] MA Li-bo, QIN Xiao-lin. Topic-Location-Category Aware Point-of-interest Recommendation [J]. Computer Science, 2020, 47(9): 81-87.
[3] BAO Yu-xuan, LU Tian-liang, DU Yan-hui. Overview of Deepfake Video Detection Technology [J]. Computer Science, 2020, 47(9): 283-292.
[4] WANG Liang, ZHOU Xin-zhi, YNA Hua. Real-time SIFT Algorithm Based on GPU [J]. Computer Science, 2020, 47(8): 105-111.
[5] LIANG Zheng-you, HE Jing-lin, SUN Yu. Three-dimensional Convolutional Neural Network Evolution Method for Facial Micro-expression Auto-recognition [J]. Computer Science, 2020, 47(8): 227-232.
[6] YANG Wei-chao, GUO Yuan-bo, LI Tao, ZHU Ben-quan. Method Based on Traffic Fingerprint for IoT Device Identification and IoT Security Model [J]. Computer Science, 2020, 47(7): 299-306.
[7] LUO Jia-lei and MENG Li-min. Signal Timing Scheme Recommendation Algorithm Based on Intersection Similarity [J]. Computer Science, 2020, 47(6A): 66-69.
[8] LAN Zhang-li, SHEN De-xing, CAO Juan and ZHANG Yu-xin. Content-independent Method for Basis Image Extraction and Image Reconstruction [J]. Computer Science, 2020, 47(6A): 226-229.
[9] ZHOU Li-peng, MENG Li-min, ZHOU Lei, JIANG Wei and DONG Jian-ping. Fall Detection Algorithm Based on BP Neural Network [J]. Computer Science, 2020, 47(6A): 242-246.
[10] YUAN De-yu, ZHANG Yi-fan, GAO Jian and SUN Hai-chun. Abnormal User Detection Method in Sina Weibo Based on User Feature Extraction [J]. Computer Science, 2020, 47(6A): 364-368.
[11] LU Ai-hong, GUO Yan, LI Ning, WANG Meng, LIU Jie. Direction-of-arrival Estimation with Two-dimensional Sparse Array Based on Atomic NormMinimization [J]. Computer Science, 2020, 47(5): 271-276.
[12] DENG Yi-jiao, ZHANG Feng-li, CHEN Xue-qin, AI Qing, YU Su-zhe. Collaborative Attention Network Model for Cross-modal Retrieval [J]. Computer Science, 2020, 47(4): 54-59.
[13] ZHU Lei, HU Qin-han, ZHAO Lei, YANG Ji-wen. Collaborative Filtering Algorithm Based on Rating Preference and Item Attributes [J]. Computer Science, 2020, 47(4): 67-73.
[14] WANG Kun-lun, LIU Wen-can, HE Xiao-hai, QING Lin-bo, WU Xiao-hong. Motion Feature Descriptor for Abnormal Behavior Detection [J]. Computer Science, 2020, 47(4): 119-124.
[15] CHEN Li-fu,LIU Yan-zhi,ZHANG Peng,YUAN Zhi-hui,XING Xue-min. Road Extraction Algorithm of Multi-feature High-resolution SAR Image Based on Multi-Path RefineNet [J]. Computer Science, 2020, 47(3): 156-161.
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .