Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (12): 181-187.doi: 10.11896/jsjkx.201100031

Special Issue: Big Data & Data Scinece

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

Multi-space Interactive Collaborative Filtering Recommendation

LI Kang-lin1,2, GU Tian-long2, BIN Chen-zhong2   

  1. 1 School of Electronic Engineering & Automation,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China
    2 Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China
  • Received:2020-11-03 Revised:2021-03-01 Online:2021-12-15 Published:2021-11-26
  • About author:LI Kang-lin,born in 1996,postgra-duate.His main research interests include recommendation system and data mining.
    BIN Chen-zhong,born in 1979,Ph.D.His main research interests include da-tamining and intelligent recommendation.
  • Supported by:
    National Natural Science Foundation of China(62066010,61862016,61966009),Natural Science Foundation of Guangxi Province(2020GXNSFAA159055) and Innovation-Driven Major Projects of Guangxi Province(AA17202024).

PDF (PC)

2670

Abstract: In the era of big data,due to information overload,it is difficult for users to find interesting content from massive data.The birth of personalized recommendation system has greatly solved this problem.Collaborative filtering has been widely used in the field of personalized recommendation,but due to the limitations of the model,the recommendation effect has not been further improved.Most existing collaborative filtering introduce presentation learning methods to obtain better user representation vectors and item representation vectors or improve user item match functions to improve the performance of the recommendation system,but such work is devoted to extracting user-item interaction information from a single interaction.This paper proposes a multi-space interactive collaborative filtering recommendation algorithm,which maps user vectors and item vectors to multiple spaces,performs user-item interaction from multiple angles,and then uses a two-layer attention mechanism to aggregate the final user representation vector and item representation vector to make a score prediction.The multi-space interaction collaborative filtering(MSICF) was compared with baseline on the published real dataset,and the evaluation of the MSICF is better than baseline.

Key words: Attention mechanism, Collaborative filtering, Multi-space interaction, Recommender systems

CLC Number: 

  • TP331
[1]XU J,HE X N,LI H.Deep learning for matching in search and recommendation[C]//The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval.2018:1365-1368.
[2]XUE H J,DAI X Y,ZHANG J B,et al.Deep Matrix Factorization Models for Recommender Systems[C]//IJCAI.2017:3203-3209.
[3]HU Q Y,HUANG L,WANG C D,et al.Item orientated recommendation by multi-view intact space learning with overlapping[J].Knowledge-Based Systems,2019,164:358-370.
[4]SRIVASTAVA R,PALSHIKAR G K,CHAURASIA S,et al.What's next? a recommendation system for industrial training[J].Data Science Engineering,2018,3(3):232-247.
[5]SARWAR B,KARYPIS G,KONSTAN J,et al.Item-based collaborative filtering recommendation algorithms[C]//Procee-dings of the 10th International Conference on World Wide Web.2001:285-295.
[6]ZHANG H W,SHEN F M,LIU W,et al.Discrete collaborative filtering[C]//Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval.2016:325-334.
[7]LINDEN G,SMITH B,YORK J.Amazon.com recommenda- tions:Item-to-item collaborative filtering[C]//IEEE Internet Computing.2003:76-80.
[8]CHEN T,ZHENG Z,LU Q,et al.Feature-based matrix factorization[J].arXiv:1109.2271,2011.
[9]KOREN Y,BELL R,VOLINSKY C.Matrix factorization techniques for recommender systems[J].Computer,2009,42(8):30-37.
[10]KOREN Y.Factorization meets the neighborhood:a multiface- ted collaborative filtering model[C]//Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining.2008:426-434.
[11]RENDLE S.Factorization machines[C]//2010 IEEE International Conference on Data Mining.2010:995-1000.
[12]BLONDEL M,FUJINO A,UEDA N,et al.Higher-order facto- rization machines[C]//Advances in Neural Information Proces-sing Systems.2016:3351-3359.
[13]WANG H,WANG N Y,YEUNG D Y.Collaborative deep lear- ning for recommender systems[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining.2015:1235-1244.
[14]HE K M,ZHANG X Y,REN S Q,et al.Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[15]SERBAN I V,SORDONI A,BENGIO Y,et al.Building end-to-end dialogue systems using generative hierarchical neural network models[C]//Thirtieth AAAI Conference on Artificial Intelligence.2016:3776-3784.
[16]HE X N,LIAO L Z,ZHANG H W,et al.Neural collaborative filtering[C]//Proceedings of the 26th International Conference on World Wide Web.2017:173-182.
[17]DENG Z H,HUANG L,WANG C D,et al.Deepcf:A unified framework of representation learning and matching function learning in recommender system[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2019:61-68.
[18]SRIVASTAVA N,HINTON G,KRIZHEVSKY A,et al. Dropout:a simple way to prevent neural networks from overfitting[J].The Journal of Machine Learning Research,2014,15(1):1929-1958.
[19]HINTON G E,SRIVASTAVA N,KRIZHEVSKY A,et al.Improving neural networks by preventing co-adaptation of feature detectors[J].arXiv:1207.0580,2012.
[20]MNIH A,SALAKHUTDINOV R R.Probabilistic matrix facto- rization[C]//Advances in Neural Information Processing Systems.2008:1257-1264.
[21]STRAHL J,PELTONEN J,MAMITSUKA H,et al.Scalable Probabilistic Matrix Factorization with Graph-Based Priors[C]//AAAI.2020:5851-5858.
[22]MONTI F,BRONSTEIN M,BRESSON X.Geometric matrix completion with recurrent multi-graph neural networks[C]//Advances in Neural Information Processing Systems.2017:3697-3707.
[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] WANG Guan-yu, ZHONG Ting, FENG Yu, ZHOU Fan. Collaborative Filtering Recommendation Method Based on Vector Quantization Coding [J]. Computer Science, 2022, 49(9): 48-54.
[3] ZHOU Fang-quan, CHENG Wei-qing. Sequence Recommendation Based on Global Enhanced Graph Neural Network [J]. Computer Science, 2022, 49(9): 55-63.
[4] DAI Yu, XU Lin-feng. Cross-image Text Reading Method Based on Text Line Matching [J]. Computer Science, 2022, 49(9): 139-145.
[5] ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
[6] XIONG Li-qin, CAO Lei, LAI Jun, CHEN Xi-liang. Overview of Multi-agent Deep Reinforcement Learning Based on Value Factorization [J]. Computer Science, 2022, 49(9): 172-182.
[7] RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207.
[8] ZHU Cheng-zhang, HUANG Jia-er, XIAO Ya-long, WANG Han, ZOU Bei-ji. Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism [J]. Computer Science, 2022, 49(8): 113-119.
[9] SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177.
[10] YAN Jia-dan, JIA Cai-yan. Text Classification Method Based on Information Fusion of Dual-graph Neural Network [J]. Computer Science, 2022, 49(8): 230-236.
[11] JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335.
[12] WANG Ming, PENG Jian, HUANG Fei-hu. Multi-time Scale Spatial-Temporal Graph Neural Network for Traffic Flow Prediction [J]. Computer Science, 2022, 49(8): 40-48.
[13] JIN Fang-yan, WANG Xiu-li. Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM [J]. Computer Science, 2022, 49(7): 179-186.
[14] XIONG Luo-geng, ZHENG Shang, ZOU Hai-tao, YU Hua-long, GAO Shang. Software Self-admitted Technical Debt Identification with Bidirectional Gate Recurrent Unit and Attention Mechanism [J]. Computer Science, 2022, 49(7): 212-219.
[15] PENG Shuang, WU Jiang-jiang, CHEN Hao, DU Chun, LI Jun. Satellite Onboard Observation Task Planning Based on Attention Neural Network [J]. Computer Science, 2022, 49(7): 242-247.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.