Computer Science ›› 2022, Vol. 49 ›› Issue (9): 48-54.doi: 10.11896/jsjkx.210700109

• Database & Big Data & Data Science • Previous Articles     Next Articles

Collaborative Filtering Recommendation Method Based on Vector Quantization Coding

WANG Guan-yu, ZHONG Ting, FENG Yu, ZHOU Fan   

  1. School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu 610054,China
  • Received:2021-07-12 Revised:2021-08-02 Online:2022-09-15 Published:2022-09-09
  • About author:WANG Guan-yu,born in 1998,postgra-duate.His main research interests include deep learning and data mining.
    ZHOU Fan,born in 1981,Ph.D,asso-ciate professor,is a member of China Computer Federation.His main research interests include machine lear-ning,spatio-temporal data mining,data mining and knowledge discovery.
  • Supported by:
    National Natural Science Foundation of China(62072077,62176043),National Key Technology Research and Development Program of the Ministry of Science and Technology of China(2019YFB1406202)and Sichuan Science and Technology Program(2020GFW068,2020ZHCG0058,2021YFQ0007,2020YFG0053).

Abstract: With the rapid development of the Internet,the emergence of massive data makes recommender system become a research hotspot in the field of computer science.Variational autoencoders(VAE) have been successfully applied to the design of collaborative filtering methods and achieved excellent recommendation results. However,there are some defects in the previous VAE-based models,such as the problems of prior constraint and the “posterior collapse”,which essentially reduce their recommendation performance.To address this issue while enabling the latent variable model more suitable for the recommendation task,a novel collaborative filtering recommendation model based on latent vector quantization is proposed in this paper.By encoding the discrete vectors instead of directly sampling from the distribution of latent variables,our method can learn discrete representations that are consistent with the observed data,which greatly improves the capability of latent vector encoding and the learning ability of the model.Extensive evaluations conducted on three benchmark datasets demonstrate the effectiveness of the proposed model.Our model can significantly improve the recommendation performance compared with existing state-of-the-art methods while learning more expressive latent representations.

Key words: Recommender system, Collaborative filtering, Vector quantization coding, Variational autoencoder

CLC Number: 

  • TP183
[1]GOPALAN P,HOFMAN J M,BLEI D M.Scalable Recommendation with Hierarchical Poisson Factorization[C]//UAI.2015:326-335.
[2]HU Y,KOREN Y,VOLINSKY C.Collaborative filtering forimplicit feedback datasets[C]//2008 Eighth IEEE International Conference on Data Mining.IEEE,2008:263-272.
[3]MNIH A,SALAKHUTDINOV R R.Probabilistic matrix fac-torization[J].Advances in Neural Information Processing Systems,2007,20:1257-1264.
[4]LIANG D,ALTOSAAR J,CHARLIN L,et al.Factorizationmeets the item embedding:Regularizing matrix factorization with item co-occurrence[C]//Proceedings of the 10th ACM Conference on Recommender Systems.2016:59-66.
[5]WU Y,DUBOIS C,ZHENG A X,et al.Collaborative denoising auto-encoders for top-n recommender systems[C]//Proceedings of the Ninth ACM International Conference on Web Search and Data Mining.2016:153-162.
[6]ELAHI E,WANG W,RAY D,et al.Variational low rank multinomials for collaborative filtering with side-information[C]//Proceedings of the 13th ACM Conference on Recommender Systems.2019:340-347.
[7]LI X,SHE J.Collaborative variational autoencoder for recommender systems[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2017:305-314.
[8]SACHDEVA N,MANCO G,RITACCO E,et al.Sequentialvariational autoencoders for collaborative filtering[C]//Procee-dings of the Twelfth ACM International Conference on Web Search and Data Mining.2019:600-608.
[9]WANG Z,CHEN C,ZHANG K,et al.Variational recurrentmodel for session-based recommendation[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management.2018:1839-1842.
[10]LIANG D,KRISHNAN R G,HOFFMAN M D,et al.Varia-tional autoencoders for collaborative filtering[C]//Proceedings of the 2018 World Wide Web Conference.2018:689-698.
[11]HE X,LIAO L,ZHANG H,et al.Neural collaborative filtering[C]//Proceedings of the 26th International Conference on World Wide Web.2017:173-182.
[12]KINGMA D P,WELLING M.Auto-encoding variational bayes[J].arXiv:1312.6114,2013.
[13]SHENBIN I,ALEKSEEV A,TUTUBALINA E,et al.Recvae:A new variational autoencoder for top-n recommendations with implicit feedback[C]//Proceedings of the 13th International Conference on Web Search and Data Mining.2020:528-536.
[14]LUCAS J,TUCKER G,GROSSE R,et al.Don't blame the Elbo! a linear Vae perspective on posterior collapse[J].arXiv:1911.02469,2019.
[15]TRAN D,BLEI D M,AIROLDI E M.Copula variational infe-rence[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems-Volume 2.2015:3564-3572.
[16]DINH L,SOHL-DICKSTEIN J,BENGIO S.Density estimation using real nvp[J].arXiv:1605.08803,2016.
[17]REZENDE D,MOHAMED S.Variational inference with normalizing flows[C]//International Conference on Machine Learning.PMLR,2015:1530-1538.
[18]OORD A,VINYALS O,KAVUKCUOGLU K.Neural discrete representation learning[J].arXiv:1711.00937,2017.
[19]OORD A,KALCHBRENNER N,VINYALS O,et al.Condi-tional image generation with PixelCNN decoders[C]//Procee-dings of the 30th International Conference on Neural Information Processing Systems.2016:4797-4805.
[20]KINGMA D P,BA J.Adam:A Method for Stochastic Optimization[C]//ICLR(Poster).2015.
[21]NING X,KARYPIS G.Slim:Sparse linear methods for top-n recommender systems[C]//2011 IEEE 11th International Conference on Data Mining.IEEE,2011:497-506.
[22]EBESU T,SHEN B,FANG Y.Collaborative memory network for recommendation systems[C]//The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval.2018:515-524.
[23]WANG X,HE X,WANG M,et al.Neural graph collaborative filtering[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval.2019:165-174.
[24]LI X,CHIN J Y,CHEN Y,et al.Sinkhorn Collaborative Filtering[C]//Proceedings of the Web Conference 2021.2021:582-592.
[1] CHENG Zhang-tao, ZHONG Ting, ZHANG Sheng-ming, ZHOU Fan. Survey of Recommender Systems Based on Graph Learning [J]. Computer Science, 2022, 49(9): 1-13.
[2] SUN Xiao-han, ZHANG Li. Collaborative Filtering Recommendation Algorithm Based on Rating Region Subspace [J]. Computer Science, 2022, 49(7): 50-56.
[3] CAI Xiao-juan, TAN Wen-an. Improved Collaborative Filtering Algorithm Combining Similarity and Trust [J]. Computer Science, 2022, 49(6A): 238-241.
[4] HE Yi-chen, MAO Yi-jun, XIE Xian-fen, GU Wan-rong. Matrix Transformation and Factorization Based on Graph Partitioning by Vertex Separator for Recommendation [J]. Computer Science, 2022, 49(6A): 272-279.
[5] GUO Liang, YANG Xing-yao, YU Jiong, HAN Chen, HUANG Zhong-hao. Hybrid Recommender System Based on Attention Mechanisms and Gating Network [J]. Computer Science, 2022, 49(6): 158-164.
[6] CHEN Zhuang, ZOU Hai-tao, ZHENG Shang, YU Hua-long, GAO Shang. Diversity Recommendation Algorithm Based on User Coverage and Rating Differences [J]. Computer Science, 2022, 49(5): 159-164.
[7] QIAO Jie, CAI Rui-chu, HAO Zhi-feng. Mining Causality via Information Bottleneck [J]. Computer Science, 2022, 49(2): 198-203.
[8] DONG Xiao-mei, WANG Rui, ZOU Xin-kai. Survey on Privacy Protection Solutions for Recommended Applications [J]. Computer Science, 2021, 48(9): 21-35.
[9] ZHAN Wan-jiang, HONG Zhi-lin, FANG Lu-ping, WU Zhe-fu, LYU Yue-hua. Collaborative Filtering Recommendation Algorithm Based on Adversarial Learning [J]. Computer Science, 2021, 48(7): 172-177.
[10] SHAO Chao, SONG Shu-mi. Collaborative Filtering Recommendation Algorithm Based on User Preference Under Trust Relationship [J]. Computer Science, 2021, 48(6A): 240-245.
[11] WU Jian-xin, ZHANG Zhi-hong. Collaborative Filtering Recommendation Algorithm Based on User Rating and Similarity of Explicit and Implicit Interest [J]. Computer Science, 2021, 48(5): 147-154.
[12] XIAO Shi-tao, SHAO Ying-xia, SONG Wei-ping, CUI Bin. Hybrid Score Function for Collaborative Filtering Recommendation [J]. Computer Science, 2021, 48(3): 113-118.
[13] HAO Zhi-feng, LIAO Xiang-cai, WEN Wen, CAI Rui-chu. Collaborative Filtering Recommendation Algorithm Based on Multi-context Information [J]. Computer Science, 2021, 48(3): 168-173.
[14] HAN Li-feng, CHEN Li. User Cold Start Recommendation Model Integrating User Attributes and Item Popularity [J]. Computer Science, 2021, 48(2): 114-120.
[15] LI Kang-lin, GU Tian-long, BIN Chen-zhong. Multi-space Interactive Collaborative Filtering Recommendation [J]. Computer Science, 2021, 48(12): 181-187.
Full text



No Suggested Reading articles found!