ESLI: Enhancing slope one recommendation through local information embedding

Autoři: Heng-Ru Zhang aff001;  Yuan-Yuan Ma aff001;  Xin-Chao Yu aff001;  Fan Min aff001
Působiště autorů: School of Computer Science, Southwest Petroleum University, Chengdu, China aff001
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article


Slope one is a popular recommendation algorithm due to its simplicity and high efficiency for sparse data. However, it often suffers from under-fitting since the global information of all relevant users/items are considered. In this paper, we propose a new scheme called enhanced slope one recommendation through local information embedding. First, we employ clustering algorithms to obtain the user clusters as well as item clusters to represent local information. Second, we predict ratings using the local information of users and items in the same cluster. The local information can detect strong localized associations shared within clusters. Third, we design different fusion approaches based on the local information embedding. In this way, both under-fitting and over-fitting problems are alleviated. Experiment results on the real datasets show that our approaches defeats slope one in terms of both mean absolute error and root mean square error.

Klíčová slova:

Clustering algorithms – Experimental design – Habits – k means clustering – Learning – Mathematical functions – Neural networks


1. Cheng WJ, Yin GS, Dong YX, Dong HB, Zhang WS. Collaborative Filtering Recommendation on Users’ Interest Sequences. PLOS ONE. 2016;11(5):1–17. doi: 10.1371/journal.pone.0155739

2. Feng JM, Fengs XY, Zhang N, Peng JY. An improved collaborative filtering method based on similarity. PLOS ONE. 2018;13(9). doi: 10.1371/journal.pone.0204003

3. Sarwar B, Karypis G, Konstan J, Riedl J. Item-based Collaborative Filtering Recommendation Algorithms. In: Proceedings of the 10th International Conference on World Wide Web; 2001. p. 285–295.

4. Sun SB, Zhang ZH, Dong XL, Zhang HR, Li TJ, Zhang L, et al. Integrating Triangle and Jaccard similarities for recommendation. PLOS ONE. 2017;12(8):1–16. doi: 10.1371/journal.pone.0183570

5. Zhou YB, Lü LY, Liu WP, Zhang JL. The Power of Ground User in Recommender Systems. PLOS ONE. 2013;8(8):1–11. doi: 10.1371/journal.pone.0070094

6. Zhao ZD, Shang MS. User-based Collaborative-Filtering Recommendation Algorithms on Hadoop. In: Proceedings of 3th International Conference on Knowledge Discovery and Data Mining; 2010. p. 478–481.

7. Linden G, Smith B, York J. Amazon. com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing. 2003;7(1):76–80. doi: 10.1109/MIC.2003.1167344

8. Lemire D, Maclachlan A. Slope One Predictors for Online Rating-Based Collaborative Filtering. In: Proceedings of the 2005 SIAM International Conference on Data Mining. SIAM; 2005. p. 471–475.

9. Keller JM, Gray MR, Givens JA. A Fuzzy K-Nearest Neighbor Algorithm. IEEE Transactions on Systems, Man, and Cybernetics. 1985;SMC-15(4):580–585. doi: 10.1109/TSMC.1985.6313426

10. Koren Y, Bell R, Volinsky C. Matrix Factorization Techniques for Recommender Systems. Computer. 2009;42(8):42–49. doi: 10.1109/MC.2009.263

11. Yu K, Schwaighofer A, Tresp V, Xu XW, Kriegel HP. Probabilistic Memory-Based Collaborative Filtering. IEEE Transactions on Knowledge and Data Engineering. 2004;16(1):56–69. doi: 10.1109/TKDE.2004.1264822

12. Demuth HB, Beale MH, De Jess O, Hagan MT. Neural network design. Hagan Martin; 2014.

13. Friedman N, Geiger D, Goldszmidt M. Bayesian Network Classifiers. Machine Learning. 1997;29(2-3):131–163. doi: 10.1023/A:1007465528199

14. Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ. LIBLINEAR: A Library for Large Linear Classification. Journal of Machine Learning Research. 2008;9(Aug):1871–1874.

15. Guo GB, Zhang J, Thalmann D. Merging trust in collaborative filtering to alleviate data sparsity and cold start. Knowledge-Based Systems. 2014;57:57–68. doi: 10.1016/j.knosys.2013.12.007

16. Shepitsen A, Gemmell J, Mobasher B, Burke R. Personalized Recommendation in Social Tagging Systems Using Hierarchical Clustering. In: Proceedings of the 2008 ACM Conference on Recommender systems; 2008. p. 259–266.

17. Zhang HR, Min F, Shi B. Regression-based three-way recommendation. Information Sciences. 2017;378:444–461. doi: 10.1016/j.ins.2016.03.019

18. Luo X, Wang D, Zhou M, Yuan H. Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2019. doi: 10.1109/TSMC.2018.2884191

19. Luo X, Zhou M, Li S, Wu D, Liu Z, Shang M. Algorithms of Unconstrained Non-negative Latent Factor Analysis for Recommender Systems. IEEE Transactions on Big Data. 2019. doi: 10.1109/TBDATA.2019.2916868

20. Herlocker JL, Konstan JA, Riedl J. Explaining Collaborative Filtering Recommendations. Proc of Cscw. 2000;22(1):5–53.

21. Zhang HR, Min F, Zhang ZH, Wang S. Efficient collaborative filtering recommendations with multi-channel feature vectors. International Journal of Machine Learning and Cybernetics. 2019;10(5):1165–1172. doi: 10.1007/s13042-018-0795-8

22. Kannan R, Ishteva M, Park H. Bounded matrix factorization for recommender system. Knowledge & Information Systems. 2014;39(3):491–511. doi: 10.1007/s10115-013-0710-2

23. Liu W, Lai HJ, Wang J, Ke GY, Yang WW, Yin J. Mix geographical information into local collaborative ranking for POI recommendation. World Wide Web. 2019; p. 1–22.

24. Zhang J, Lin YJ, Lin ML, Liu JH. An effective collaborative filtering algorithm based on user preference clustering. Applied Intelligence. 2016;45(2):230–240. doi: 10.1007/s10489-015-0756-9

25. Chen C, Li DS, Lv Q, Yan JC, Chu SM, Shang L. MPMA: Mixture Probabilistic Matrix Approximation for Collaborative Filtering. In: IJCAI; 2016. p. 1382–1388.

26. Chen C, Li DS, Lv Q, Yan JC, Shang L, Chu SM. GLOMA: Embedding global information in local matrix approximation models for collaborative filtering. In: Thirty-First AAAI Conference on Artificial Intelligence; 2017.

27. Hartigan JA. Clustering Algorithms. Applied Statistics. 1975;25(1).

28. Tellaroli P, Bazzi M, Donato M, Brazzale AR, Drăghici S. Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters. PLOS ONE. 2016;11(3). doi: 10.1371/journal.pone.0152333 27015427

29. Gordon MD. User-based document clustering by redescribing subject descriptions with a genetic algorithm. Journal of the American Society for Information Science. 1991;42(5):311–322. doi: 10.1002/(SICI)1097-4571(199106)42:5%3C311::AID-ASI1%3E3.0.CO;2-J

30. Hartigan JA, Wong MA. Algorithm AS 136: A K-Means Clustering Algorithm. Journal of the Royal Statistical Society Series C (Applied Statistics). 1979;28(1):100–108.

31. Zheng M, Min F, Zhang HR, Chen WB. Fast Recommendations With the M-Distance. IEEE Access. 2016;4:1464–1468. doi: 10.1109/ACCESS.2016.2549182

32. Ma H, Zhou D, Liu C, Lyu MR, King I. Recommender systems with social regularization. In: Proceedings of the fourth ACM international conference on Web search and data mining. WSDM’11. Hong Kong, China; 2011. p. 287–296.

33. Konno H, Yamazaki H. MEAN-ABSOLUTE DEVIATION PORTFOLIO OPTIMIZATION MODEL AND ITS APPLICATIONS TO TOKYO STOCK MARKET. Management Science. 1991;37(5):519–531. doi: 10.1287/mnsc.37.5.519

34. Willmott CJ, Matsuura K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research. 2005;30(1):79–82. doi: 10.3354/cr030079

35. Levinson N. The Wiener (Root Mean Square) Error Criterion in Filter Design and Prediction. Journal of Mathematics and Physics. 1946;25(1-4):261–278. doi: 10.1002/sapm1946251261

36. Zhang HR, Min F, Wu YX, Fu ZL, Gao L. Magic barrier estimation models for recommended systems under normal distribution. Appl Intell. 2018;48(12):4678–4693. doi: 10.1007/s10489-018-1237-8

37. Xu WH, Li WT, Zhang XT. Generalized multigranulation rough sets and optimal granularity selection. Granular Computing. 2017;2(4):271–288. doi: 10.1007/s41066-017-0042-9

38. Liu Y, Liao SZ. Granularity selection for cross-validation of SVM. Information Sciences. 2017;378:475–483. doi: 10.1016/j.ins.2016.06.051

39. Zhu PF, Hu QH. Adaptive neighborhood granularity selection and combination based on margin distribution optimization. Information Sciences. 2013;249:1–12. doi: 10.1016/j.ins.2013.06.012

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2019 Číslo 10
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