Computer Science ›› 2018, Vol. 45 ›› Issue (11A): 439-444.

• Big Data & Data Mining • Previous Articles     Next Articles

Local Model Weighted Ensemble for Top-N Movie Recommendation

TANG Ying1, SUN Kang-gao1, QIN Xu-jia1, ZHOU Jian-mei2   

  1. School of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China1
    School of Computer Science and Technology,Nantong University,Nantong,Jiangsu 226019,China2
  • Online:2019-02-26 Published:2019-02-26

Abstract: In order to solve the problem that the traditional recommendation algorithms can not accurately capture the user preference with a single model,this paper proposed a Top-N personalized recommendation algorithm based on local model weighted ensemble.This recommendation algorithm adopts user clustering to compute the local models and takes the sparse linear model as the basic recommendation model.Meanwhile,the semantic-level feature vector representation of each user was proposed based on LDA topic model and movie text content information,so as to implement user clustering.The experiments of the film data crawled from Douban show that our local model weighted ensemble recommendation algorithm enhances the recommendation quality of the original base model and outperforms some traditional classical recommendation algorithms,which demonstrates the effectiveness of the proposed algorithm.

Key words: Recommendation system, Model ensemble, Sparse linear model, Topic model

CLC Number: 

  • TP301
[1]DESHPANDE M,KARYPIS G.Item-based top-n recommendation algorithms[J].ACM Transactions on Information Systems (TOIS),2004,22(1):143-177.
[2]NING X,KARYPIS G.Slim:Sparse linear methods for top-n recommender systems[C]∥2011 IEEE 11th International Conference on Data Mining (ICDM).IEEE,2011:497-506.
[3]FUNK S.Netflix Update:Try This at Home[OL].http://si-fter.org/~simon/journal/20061211.html.
[4]KOREN Y,BELL R,VOLINSKY C.Matrix factorization techniques for recommender systems[J].Computer,2009,42(8):30-37.
[5]KOREN Y.Factorization meets the neighborhood:a multiface-ted collaborative filtering model[C]∥Proceedings of the 14th ACM SIGKDD international conference on Knowledge Discoveryand Data Mining.ACM,2008:426-434.
[6]KOREN Y.Collaborative filtering with temporal dynamics[J].Communications of the ACM,2010,53(4):89-97.
[7]HU Y,KOREN Y,VOLINSKY C.Collaborative filtering for implicit feedback datasets[C]∥Eighth IEEE International Conference on Data Mining,2008(ICDM’08).IEEE,2008:263-272.
[8]KABBUR S,NING X,KARYPIS G.Fism:factored item simila-rity models for top-n recommender systems[C]∥Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2013:659-667.
[9]CREMONESI P,KOREN Y,TURRIN R.Performance of re-commender algorithms on top-n recommendation tasks[C]∥Proceedings of the Fourth ACM Conference on Recommender Systems.ACM,2010:39-46.
[10]KANG Z,PENG C,CHENG Q.Top-N Recommender System via Matrix Completion[C]∥AAAI.2016:179-185.
[11]RENDLE S,FREUDENTHALER C,GANTNER Z,et al.BPR:Bayesian personalized ranking from implicit feedback[C]∥Proceedings of the Twenty-fifth Conference on Uncertainty in Artificial Intelligence.AUAI Press,2009:452-461.
[12]O’CONNOR M,HERLOCKER J.Clustering items for collaborative filtering[C]∥Proceedings of the ACM SIGIR Workshop on Recommender Systems.UC Berkeley,1999:128.
[13]XU B,BU J,CHEN C,et al.An exploration of improving colla-borative recommender systems via user-item subgroups[C]∥Proceedings of the 21st International Conference on World Wide Web.ACM,2012:21-30.
[14]LEE J,KIM S,LEBANON G,et al.Local low-rank matrix approximation[C]∥International Conference on Machine Lear-ning.2013:82-90.
[15]LEE J,BENGIO S,KIM S,et al.Local collaborative ranking[C]∥Proceedings of the 23rd International Conference on World Wide Web.ACM,2014:85-96.
[16]CHRISTAKOPOULOU E,KARYPIS G.Local item-item mo-dels for top-n recommendation [C]∥Proceedings of the 10th ACM Conference on Recommender Systems.ACM,2016:67-74.
[17]BLEI D M,NG A Y,JORDAN M I.Latent dirichlet allocation[J].Journal of Machine Learning Research,2003,3(1):993-1022.
[18]HOFFMAN M,BACH F R,BLEI D M.Online learning for latent dirichlet allocation[C]∥Advances in Neural Information Processing Systems.2010:856-864.
[19]SEDHAIN S,MENON A K,SANNER S,et al.Autorec:Autoencoders meet collaborative filtering[C]∥Proceedings of the 24th International Conference on World Wide Web.ACM,2015:111-112.
[20]CHENG H T,KOC L,HARMSEN J,et al.Wide & deep lear-ning for recommender systems[C]∥Proceedings of the 1st Workshop on Deep Learning for Recommender Systems.ACM,2016:7-10.
[1] MA Li-bo, QIN Xiao-lin. Topic-Location-Category Aware Point-of-interest Recommendation [J]. Computer Science, 2020, 47(9): 81-87.
[2] LV Ze-yu, LI Ji-xuan, CHEN Ru-Jian and CHEN Dong-ming. Research on Prediction of Re-shopping Behavior of E-commerce Customers [J]. Computer Science, 2020, 47(6A): 424-428.
[3] LI Tai-song,HE Ze-yu,WANG Bing,YAN Yong-hong,TANG Xiang-hong. Session-based Recommendation Algorithm Based on Recurrent Temporal Convolutional Network [J]. Computer Science, 2020, 47(3): 103-109.
[4] ZHOU Bo. Bipartite Network Recommendation Algorithm Based on Semantic Model [J]. Computer Science, 2020, 47(11A): 482-485.
[5] WANG Han, XIA Hong-bin. Collaborative Filtering Recommendation Algorithm Mixing LDA Model and List-wise Model [J]. Computer Science, 2019, 46(9): 216-222.
[6] GUO Xu, ZHU Jing-hua. Deep Neural Network Recommendation Model Based on User Vectorization Representation and Attention Mechanism [J]. Computer Science, 2019, 46(8): 111-115.
[7] JU Ya-ya, YANG Lu, YAN Jian-feng. LDA Algorithm Based on Dynamic Weight [J]. Computer Science, 2019, 46(8): 260-265.
[8] ZHANG Lei,CAI Ming. Image Annotation Based on Topic Fusion and Frequent Patterns Mining [J]. Computer Science, 2019, 46(7): 246-251.
[9] WANG Xu, PANG Wei, WANG Zhe. MetaStruct-CF:A Meta Structure Based Collaborative Filtering Algorithm in Heterogeneous Information Networks [J]. Computer Science, 2019, 46(6A): 397-401.
[10] LIU Qing-qing, LUO Yong-long, WANG Yi-fei, ZHENG Xiao-yao, CHEN Wen. Hybrid Recommendation Algorithm Based on SVD Filling [J]. Computer Science, 2019, 46(6A): 468-472.
[11] SHI Xiao-ling, CHEN Zhi, YANG Li-gong, SHEN Wei. Matrix Factorization Recommendation Algorithm Based on Adaptive Weighted Samples [J]. Computer Science, 2019, 46(6A): 488-492.
[12] FAN Dao-yuan, SUN Ji-hong, WANG Wei, TU Ji-ping, HE Xin. Detection Method of Duplicate Defect Reports Fusing Text and Categorization Information [J]. Computer Science, 2019, 46(12): 192-200.
[13] ZHAO Hai-yan, WANG Jing, CHEN Qing-kui, CAO Jian. Application of Active Learning in Recommendation System [J]. Computer Science, 2019, 46(11A): 153-158.
[14] ZHU Zhi-cheng, LIU Jia-wei, YAN Shao-hong. Research and Application of Multi-label Learning in Intelligent Recommendation [J]. Computer Science, 2019, 46(11A): 189-193.
[15] JIA Ning, ZHENG Chun-jun. Model of Music Theme Recommendation Based on Attention LSTM [J]. Computer Science, 2019, 46(11A): 230-235.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] . [J]. Computer Science, 2018, 1(1): 1 .
[2] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[3] 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 .
[4] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[5] 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 .
[6] 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 .
[7] 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 .
[8] 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 .
[9] 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 .
[10] 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 .