Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 238-241.doi: 10.11896/jsjkx.210400088

• Big Data & Data Science • Previous Articles     Next Articles

Improved Collaborative Filtering Algorithm Combining Similarity and Trust

CAI Xiao-juan1, TAN Wen-an1,2   

  1. 1 College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
    2 School of Computer and Information Engineering,Shanghai Polytechnic University,Shanghai 201209,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:CAI Xiao-juan,born in 1997,postgra-duate.Her main research interests include recommendation system and so on.
    TAN Wen-an,born in 1965,Ph.D,professor.His main research interests include software service engineering,trustworthy service computing and composition,collaborative computing,business process intelligence technology.
  • Supported by:
    National Natural Science Foundation of China(61672022,61272036,U1904186) and Foundation of the Key Disciplines of Shanghai Polytechnic University(XXKZD1604).

Abstract: The rapid development of e-commerce not only gives consumers more choice but has also causes information overload.As an indispensable method in information filtering technology,recommendation system has been widely concerned by the society.Collaborative filtering algorithm is the most widely used technology in recommendation systems,but it faces problems such as data sparsity,cold start and data scalability.This paper proposes an improved collaborative filtering algorithm model based on the fusion of trusted values and user similarity.This algorithm comprises three steps:first,we calculate the trust values between users;then we calculate the similarity between users;at last,we integrate the trust and the similarity to re-calculate the trust value between users and get the final rating prediction equation.Experimental results show that for different neighborhood sets,the performance of the proposed algorithm is better than that of traditional collaborative filtering algorithms.

Key words: Collaborative filtering, E-commerce, Recommendation system, Similarity, Trust

CLC Number: 

  • TP182
[1] 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).IEEE,2016:2431-2434.
[2] LI D,CHEN C,LV Q,et al.An algorithm for efficient privacy-preserving item-based collaborative filtering[J].Future Generation Computer Systems,2016,55:311-320.
[3] KOOHI H,KIANI K.User based Collaborative Filtering using fuzzy C-means[J].Measurement,2016,91:134-139.
[4] SHI Y,LARSON M,HANJALIC A.Exploiting user similarity based on rated-item pools for improved user-based collaborative filtering[C]//Proceedings of the Third ACM Conference on Recommender Systems.2009:125-132.
[5] JIANG L,CHENG Y,YANG L,et al.A trust-based collaborative filtering algorithm for E-commerce recommendation system[J].Journal of Ambient Intelligence and Humanized Computing,2019,10(8):3023-3034.
[6] DURICIC T,LACIC E,KOWALD D,et al.Trust-based collaborative filtering:Tackling the cold start problem using regular equivalence[C]//Proceedings of the 12th ACM Conference on Recommender Systems.2018:446-450.
[7] BELLOGÍN A,CASTELLS P,CANTADOR I.Improving me-mory-based collaborative filtering by neighbour selection based on user preference overlap[C]//Proceedings of the 10th Confe-rence on Open Research Areas in Information Retrieval.2013:145-148.
[8] THORAT P B,GOUDAR R M,BARVE S.Survey on collabo-rative filtering,content-based filtering and hybrid recommendation system[J].International Journal of Computer Applications,2015,110(4):31-36.
[9] SMITH B,LINDEN G.Two decades of recommender systems at Amazon.com[J].IEEE Internet Computing,2017,21(3):12-18.
[10] 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).IEEE,2018:102-106.
[11] LIU X J.An improved clustering-based collaborative filteringrecommendation algorithm[J].Cluster Computing,2017,20(2):1281-1288.
[12] XU J,LI X B,XU H.Research and analysis of collaborative recommendation algorithm based on user trust[J].Data Communication,2019,2:29-34.
[13] MANUEL P.A trust model of cloud computing based on Quality of Service[J].Annals of Operations Research,2015,233(OCT.):281-292.
[14] WANG W,LU Y.Analysis of the mean absolute error(MAE) and the root mean square error(RMSE) in assessing rounding model[C]//IOP Conference Series:Materials Science and Engineering.2018.
[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] CHAI Hui-min, ZHANG Yong, FANG Min. Aerial Target Grouping Method Based on Feature Similarity Clustering [J]. Computer Science, 2022, 49(9): 70-75.
[4] ZHENG Wen-ping, LIU Mei-lin, YANG Gui. Community Detection Algorithm Based on Node Stability and Neighbor Similarity [J]. Computer Science, 2022, 49(9): 83-91.
[5] WU Zi-yi, LI Shao-mei, JIANG Meng-han, ZHANG Jian-peng. Ontology Alignment Method Based on Self-attention [J]. Computer Science, 2022, 49(9): 215-220.
[6] QIN Qi-qi, ZHANG Yue-qin, WANG Run-ze, ZHANG Ze-hua. Hierarchical Granulation Recommendation Method Based on Knowledge Graph [J]. Computer Science, 2022, 49(8): 64-69.
[7] FANG Yi-qiu, ZHANG Zhen-kun, GE Jun-wei. Cross-domain Recommendation Algorithm Based on Self-attention Mechanism and Transfer Learning [J]. Computer Science, 2022, 49(8): 70-77.
[8] LI Bin, WAN Yuan. Unsupervised Multi-view Feature Selection Based on Similarity Matrix Learning and Matrix Alignment [J]. Computer Science, 2022, 49(8): 86-96.
[9] SHUAI Jian-bo, WANG Jin-ce, HUANG Fei-hu, PENG Jian. Click-Through Rate Prediction Model Based on Neural Architecture Search [J]. Computer Science, 2022, 49(7): 10-17.
[10] QI Xiu-xiu, WANG Jia-hao, LI Wen-xiong, ZHOU Fan. Fusion Algorithm for Matrix Completion Prediction Based on Probabilistic Meta-learning [J]. Computer Science, 2022, 49(7): 18-24.
[11] SUN Xiao-han, ZHANG Li. Collaborative Filtering Recommendation Algorithm Based on Rating Region Subspace [J]. Computer Science, 2022, 49(7): 50-56.
[12] ZENG Zhi-xian, CAO Jian-jun, WENG Nian-feng, JIANG Guo-quan, XU Bin. Fine-grained Semantic Association Video-Text Cross-modal Entity Resolution Based on Attention Mechanism [J]. Computer Science, 2022, 49(7): 106-112.
[13] DU Hang-yuan, LI Duo, WANG Wen-jian. Method for Abnormal Users Detection Oriented to E-commerce Network [J]. Computer Science, 2022, 49(7): 170-178.
[14] HUANG Shao-bin, SUN Xue-wei, LI Rong-sheng. Relation Classification Method Based on Cross-sentence Contextual Information for Neural Network [J]. Computer Science, 2022, 49(6A): 119-124.
[15] WANG Yi, LI Zheng-hao, CHEN Xing. Recommendation of Android Application Services via User Scenarios [J]. Computer Science, 2022, 49(6A): 267-271.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!