Information Filtering via a Scaling-Based Function

PLoS ONE

Volumn 8, Issue 5, 2013, Pages

  Qiu, Tian ^a   Zhang, Zi Ke ^b,c,d   Chen, Guang ^a  

^a NANCHANG HANGKONG UNIVERSITY   (China)

^b HANGZHOU NORMAL UNIVERSITY   (China)

^c UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^d BEIJING COMPUTATIONAL SCIENCE RESEARCH CENTER   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ANALYTIC METHOD; ARTICLE; BIODIVERSITY; DATA BASE; INFORMATION PROCESSING; MATHEMATICAL ANALYSIS; PROBABILITY; PROCESS OPTIMIZATION; SCALING BASED ALGORITHM;

ALGORITHMS; DATA INTERPRETATION, STATISTICAL; DIFFUSION; HUMANS; MODELS, STATISTICAL; PROBABILITY; SOFTWARE; THERMAL CONDUCTIVITY;

EID: 84877868447     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0063531     Document Type: Article

References (51)

1. Adomavicius G, Tuzhilin A, (2005) Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans KnowlData Eng 17: 734-749.
2. Goldberg D, Nichols D, Oki BM, Terry D, (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35: 61-70.
3. Schafer JB, Frankowski D, Herlocker J, Sen S (2007) Collaborative filtering recommender systems. In: The adaptive web, Springer. 291-324.
4. Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proc. 14th Conf. Uncertainity Artif. Intel. Morgan Kaufmann Publishers Inc., 43-52.
5. Nakamura A, Abe N (1998) Collaborative filtering using weighted majority prediction algorithms. In: Proc. 5th Intl. Conf. Mach. Learn. 395-403.
6. Delgado J, Ishii N (1999) Memory-based weighted majority prediction. In: SIGIR Workshop Recomm. Syst. Citeseer.
7. Getoor L, Sahami M (1999) Using probabilistic relational models for collaborative filtering. In: Workshop Web Usage Anal. User Profil. Citeseer.
8. Hofmann T (2003) Collaborative filtering via gaussian probabilistic latent semantic analysis. In: Proc. 26th Ann. Intl. SIGIR Conf. Research Devel. Infor. Retr. ACM, 259-266.
9. Billsus D, Pazzani MJ, (2000) User modeling for adaptive news access. User Model User-Adap 10: 147-180.
10. Marlin B (2003) Modeling user rating profiles for collaborative filtering. Adv Neural inf Process Syst 16.
11. Pazzani MJ, Billsus D (2007) Content-based recommendation systems. In: The adaptive web, Springer. 325-341.
12. Lipczak M, Hu Y, Kollet Y, Milios E (2009) Tag sources for recommendation in collaborative tagging systems. Proc ECML/PKDD Discovery Challenge: 157-172.
13. Cantador I, Vallet D, Jose JM (2009) Measuring vertex centrality in co-occurrence graphs for online social tag recommendation. Proc ECML/PKDD Discovery Challenge: 17-33.
14. Ju S, Hwang KB (2009) A weighting scheme for tag recommendation in social bookmarking systems. In: Proc. ECML/PKDD Discovery Challenge. 109-118.
15. Balabanović M, Shoham Y, (1997) Fab: content-based, collaborative recommendation. Comm ACM 40: 66-72.
16. Goldberg K, Roeder T, Gupta D, Perkins C, (2001) Eigentaste: A constant time collaborative filtering algorithm. Infor Retr 4: 133-151.
17. Hofmann T, (2004) Latent semantic models for collaborative filtering. ACM Trans Inf Syst 22: 89-115.
18. Blei DM, Ng AY, Jordan MI, (2003) Latent dirichlet allocation. J Mach Learn Res 3: 993-1022.
19. Laureti P, Moret L, Zhang YC, Yu YK, (2006) Information filtering via iterative refinement. EPL 75: 1006.
20. Ren J, Zhou T, Zhang YC, (2008) Information filtering via self-consistent refinement. EPL 82: 58007.
21. Zhang ZK, Zhou T, Zhang YC, (2011) Tag-aware recommender systems: a state-of-the-art survey. J Comput Sci Technol 26: 767-777.
22. Lü L, Medo M, Yeung CH, Zhang YC, Zhang ZK, et al. (2012) Recommender systems. Phys Rep 519: 1-49.
23. Zhang YC, Blattner M, Yu YK, (2007) Heat conduction process on community networks as a recommendation model. Phys Rev Lett 99: 154301.
24. Zhou T, Ren J, Medo M, Zhang YC, (2007) Bipartite network projection and personal recommendation. Phys Rev E 76: 046115.
25. Liu C, Zhou WX, (2012) Heterogeneity in initial resource configurations improves network-based hybrid recommendation algorithm. Physica A 391: 5704-5711.
26. Zhou T, Su RQ, Liu RR, Jiang LL, Wang BH, et al. (2009) Accurate and diverse recommendations via eliminating redundant correlations. New J Phys 11: 123008.
27. Liu J, Deng G, (2009) Link prediction in a user-object network based on time-weighted resource allocation. Physica A 388: 3643-3650.
28. Zhou T, Kuscsik Z, Liu JG, Medo M, Wakeling JR, et al. (2010) Solving the apparent diversityaccuracy dilemma of recommender systems. Proc Natl Acad Sci USA 107: 4511-4515.
29. Liu RR, Jia CX, Zhou T, Sun D, Wang BH, (2009) Personal recommendation via modified collaborative filtering. Physica A 388: 462-468.
30. Zhang ZK, Zhou T, Zhang YC, (2010) Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs. Physica A 389: 179-186.
31. Shang MS, Zhang ZK, Zhou T, Zhang YC, (2010) Collaborative filtering with diffusion-based similarity on tripartite graphs. Physica A 389: 1259-1264.
32. Liu JG, Zhou T, Wang BH, Zhang YC, Guo Q, (2009) Effects of user's tastes on personalized recommendation. Int J Mod Phys C 20: 1925-1932.
33. Liu JG, Zhou T, Che HA, Wang BH, Zhang YC, (2010) Effects of high-order correlations on personalized recommendations for bipartite networks. Physica A 389: 881-886.
34. Zeng A, Yeung CH, Shang MS, Zhang YC, (2012) The reinforcing influence of recommendations on global diversification. EPL 97: 18005.
35. Liu JG, Zhou T, Guo Q, (2011) Information filtering via biased heat conduction. Phys Rev E 84: 037101.
36. Liang H, Xu Y, Li Y, Nayak R, Tao X (2010) Connecting users and items with weighted tags for personalized item recommendations. In: Proc. 21st ACM Conf. Hypertext hypermedia. ACM, 51-60.
37. Cheng F, Liu C, Jiang J, Lu W, Li W, et al. (2012) Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput Biol 8: e1002503.
38. Qiu T, Chen G, Zhang ZK, Zhou T, (2011) An item-oriented recommendation algorithm on coldstart problem. EPL 95: 58003.
39. Zhou T, Jiang LL, Su RQ, Zhang YC, (2008) Effect of initial configuration on network-based recommendation. EPL 81: 58004.
40. Lü L, Liu W, (2011) Information filtering via preferential diffusion. Phys Rev E 83: 066119.
41. Zhang ZK, Liu C, Zhang YC, Zhou T, (2010) Solving the cold-start problem in recommender systems with social tags. EPL 92: 28002.
42. Ahn HJ, (2008) A new similarity measure for collaborative filtering to alleviate the new user coldstarting problem. Inf Sci 178: 37-51.
43. Herlocker JL, Konstan JA, Terveen LG, Riedl JT, (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22: 5-53.
44. Papagelis M, Plexousakis D, (2005) Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents. Engin Appl Artif Intel 18: 781-789.
45. Lam XN, Vu T, Le TD, Duong AD (2008) Addressing cold-start problem in recommendation systems. In: Proc. 2nd Intl. Conf. Ubiquitous Infor. Manag. Commun. ACM, 208-211.
46. Park YJ, Tuzhilin A (2008) The long tail of recommender systems and how to leverage it. In: Proc. 2008 ACM Conf. Recomm. Syst. ACM, 11-18.
47. Jamali M, Ester M (2009) Trustwalker: a random walk model for combining trust-based and itembased recommendation. In: Proc. 15th ACM SIGKDD Intl Conf. Knowl. Disc. Data Mining. ACM, 397-406.
48. Groh G, Ehmig C (2007) Recommendations in taste related domains: collaborative filtering vs. social filtering. In: Proc. 2007 Intl. Conf. Supporting Group Work. ACM, 127-136.
49. Zhang ZK, Liu C, (2012) Hybrid recommendation algorithm based on two roles of social tags. Int J Bifurcat Chaos 22: 1250166.
50. Kim HN, Ji AT, Ha I, Jo GS, (2010) Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation. Electron Commerce Research Appl 9: 73-83.
51. Chu W, Park ST (2009) Personalized recommendation on dynamic content using predictive bilinear models. In: Proc. 18th Intl. Conf. World Wide Web. ACM, 691-700.