中文

Journal of Applied Sciences >

2023 , Vol. 41 >Issue 6: 940 - 957

DOI: https://doi.org/10.3969/j.issn.0255-8297.2023.06.003

Communication Engineering

Recommendation Algorithm Based on User Similarity Selection and Label Distance

Expand
  • School of Optical-Electronic and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Received date: 2022-05-24

  Online published: 2023-11-30

Fold

Abstract

Neighbor selection and item label information have important influence on rating prediction of recommendation system. To improve the accuracy and scalability of recommendation systems, this paper proposes a distance-model based approach that utilizes user similarity selection and label distance. First, the users with similarity greater than the threshold value are selected as the neighbors of the users to be predicted to deal with insufficient scalability of the algorithm. Second, the user’s rating of the item is mapped to the user’s rating of the item label using the label information to enhance the accuracy. Users’ ratings of movies were predicted by using discount validation in both movie datasets. Experimental results show that the accuracy and scalability of the recommendation algorithm based on user similarity selection and label distance are greatly improved.

Key words： recommendation system; collaborative filtering; similarity screening; label distance

Cite this article

SU Zhan, CHEN Xueqian, AI Jun, HUANG Zhong . Recommendation Algorithm Based on User Similarity Selection and Label Distance[J]. Journal of Applied Sciences, 2023 , 41(6) : 940 -957 . DOI: 10.3969/j.issn.0255-8297.2023.06.003

References

[1] JALiLi M, AhMADiAn S, IzADi M, et al. Evaluating collaborative filtering recommender algorithms:a survey[J]. IEEE Access, 2018, 6:74003-74024.
[2] 苏湛,王佳伟,艾均,等.相似性与置信系数为基础的推荐系统评分预测[J].小型微型计算机系统,2021, 42(5):984-989.Su Z, WAnG J W, Ai J, et al. Rating prediction in recommendation systems based on similarity and confidence coefficient[J]. Journal of Chinese Computer Systems, 2021, 42(5):984-989.(in Chinese)
[3] KATARTA R, VERMA O P. Recommender system with grey wolf optimizer and FCM[J]. Neural Computing and Applications, 2018, 30(5):1679-1687.
[4] AnAnD A, RAM M. System reliability management:solutions and technologies[M]. Boca Raton,Florida, USA:CRC Press, 2019:132-148.
[5] Li D S, LüQ, ShAnG L, et al. Efficient privacy-preserving content recommendation for online social communities[J]. Neurocomputing, 2017, 219:440-454.
[6] XionG R B, WAnG J, ZhAnG N, et al. Deep hybrid collaborative filtering for web service recommendation[J]. Expert Systems with Applications, 2018, 110:191-205.
[7] Hu R, Dou W C, Liu J X. ClubCF:a clustering-based collaborative filtering approach for big data application[J]. IEEE Transactions on Emerging Topics in Computing, 2014, 2(3):302-313.
[8] 苏湛,林祖夷,艾均.类型相似性与品味影响力的推荐系统评分预测[J].小型微型计算机系统, 2021,42(12):2530-2537.Su Z, Lin Z Y, Ai J. Rating prediction in recommender systems based on type similarity and taste influence[J]. Journal of Chinese Computer Systems, 2021, 42(12):2530-2537.(in Chinese)
[9] Ro BiLLARD M, WALkER R, ZiMMERMAnn T. Recommendation systems for software engineering[J]. IEEE Software, 2010, 27(4):80-86.
[10] Guo T L, Luo J Z, DonG K, et al. Locally differentially private item-based collaborative filtering[J]. Information Sciences, 2019, 502:229-246.
[11] Li N, Li C P. Accumulative influence weight collaborative filtering recommendation approach[M]//Opportunities and Challenges for Next-Generation Applied Intelligence. Berlin, Heidelberg:Springer, 2009:73-78.
[12] Ai J, CAi Y F, Su Z, et al. Predicting user-item links in recommender systems based on similarity-network resource allocation[J]. Chaos, Solitons&Fractals, 2022, 158:112032.
[13] Li K K, Zhou X Z, Lin F, et al. Deep probabilistic matrix factorization framework for online collaborative filtering[J]. IEEE Access, 2019, 7:56117-56128.
[14] LAnGsETh H, NiELsEn T D. A latent model for collaborative filtering[J]. International Journal of Approximate Reasoning, 2012, 53(4):447-466.
[15] LEMiRE D, MAcLAchLAn A. Slope one predictors for online rating-based collaborative filtering[C]//2005 SIAM International Conference on Data Mining, 2005:471-475.
[16] 马鑫,吴云,鹿泽光.基于混合神经网络的协同过滤推荐模型[J].应用科学学报, 2020, 38(3):478-487.MA X, Wu Y, Lu Z G. Collaborative filtering recommendation model based on hybrid neural network[J]. Journal of Applied Sciences, 2020, 38(3):478-487.(in Chinese)
[17] BATMAz Z, YuREkLi A, BiLGE A, et al. A review on deep learning for recommender systems:challenges and remedies[J]. Artificial Intelligence Review, 2019, 52(1):1-37.
[18] Joy J, PiLLAi R V G. Review and classification of content recommenders in E-learning environment[J]. Journal of King Saud University-Computer and Information Sciences, 2022, 34(9):7670-7685.
[19] Mu R H. A survey of recommender systems based on deep learning[J]. IEEE Access, 2018, 6:69009-69022.
[20] WiLLMoTT C J, MATsuuRA K. Advantages of the mean absolute error(MAE)over the root mean square error(RMSE)in assessing average model performance[J]. Climate Research, 2005,30:79-82.
[21] HE X S, Zhou M Y, Zhuo Z, et al. Predicting online ratings based on the opinion spreading process[J]. Physica A:Statistical Mechanics and Its Applications, 2015, 436:658-664.
[22] JAvARi A, GhARiBshAh J, JALiLi M. Recommender systems based on collaborative filtering and resource allocation[J]. Social Network Analysis and Mining, 2014, 4(1):234.
[23] Ai J, Liu Y Y, Su Z, et al. Link prediction in recommender systems based on multi-factor network modeling and community detection[J]. EPL(Europhysics Letters), 2019, 126(3):38003.
[24] SinGh P K, SinhA M, DAs S, et al. Enhancing recommendation accuracy of item-based collaborative filtering using Bhattacharyya coefficient and most similar item[J]. Applied Intelligence,2020, 50(12):4708-4731.
[25] LEE S. Using entropy for similarity measures in collaborative filtering[J]. Journal of Ambient Intelligence and Humanized Computing, 2020, 11(1):363-374.
[26] Su Z, Lin Z Y, Ai J, et al. Rating prediction in recommender systems based on user behavior probability and complex network modeling[J]. IEEE Access, 2021, 9:30739-30749.
[27] KLuvER D, EksTRAnD M D, KonsTAn J A. Rating-based collaborative filtering:algorithms and Evaluation[M]//Social Information Access. Cham:Springer, 2018:344-390.
[28] WAnG Y, WAnG L, Li Y, et al. A theoretical analysis of NDCG ranking measures[C]//26th Annual Conference on Learning Theory, 2013, 8:6.
Options
Outlines

/

〈 〉