Abstract
Recommender systems have become increasingly essential in fields where mass personalization is highly valued. In this paper, we propose a model based on the analysis of the similarity between the new item and the object that the users have selected to solve cold-start problem in network-based recommendation. In order to improve the accuracy of the model, we take the degree of the items that have been collected by the user into consideration. The experiments with MovieLens data set indicate substantial improvements of this model in overcoming the cold-start problem in network-based recommendation.
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References
Broder A, Kumar R, Moghoul F, Raghavan P, Rajagopalan S, Stata R, Tomkins A, Wiener J (2000) Comput Netw 33:309
Schein AI, Popescul A, Ungar LH, Pennock DM (2002) In: SIGIR ‘02 Proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval, pp 253–260
Adomavicius G, Tuzhilin A (2005) IEEE Trans Knowl Data Eng 17:734
Zhou T, Ren J, Medo M, Zhang Y-C (2007) Bipartite network projection and personal recommendation. Phys Rev E 76:046115
Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) ACM Trans Inf Syst 22:5
Ren J, Zhou T, Zhang Y-C (2008) Europhys Lett 82:58007
Zhou T, Jiang L-L, Su R-Q, Zhang Y-C (2008) Europhys Lett 81:58004
Zhang Y-C, Medo M, Ren J, Zhou T, Li T, Yang F (2007) Europhys Lett 80:68003
Liu C, Zhou W-X (2012) Heterogeneity in initial resource configurations improves a network-based hybrid recommendation algorithm. Physica A, pp 5704–5711
The MovieLens data can be downloaded from the website of GroupLens Research. http://www.grouplens.org
Blattner M, Zhang Y-C, Maslov S (2007) Phys A 373:753
Said A, Jain BJ, Albayrak S (2012) Analyzing weighting schemes in collaborative filtering: cold start, post cold start and power users. In: SAC ‘12 Proceedings of the 27th annual ACM symposium on applied computing, pp 2035–2040
Acknowledgments
This work has been supported by the National Natural Science Foundation of China under Grant 61172072, 61271308, the Fundamental Research Funds for the Central Universities under Grant 2013JBM006, the Beijing Natural Science Foundation under Grant 4112045, the Research Fund for the Doctoral Program of Higher Education of China under Grant W11C100030, the Beijing Science and Technology Program under Grant Z121100000312024. The authors are also grateful for the comments and suggestions of the reviewers.
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Liu, Y., Jia, F., Cao, W. (2014). A Degree-Based Method to Solve Cold-Start Problem in Network-Based Recommendation. In: Huang, YM., Chao, HC., Deng, DJ., Park, J. (eds) Advanced Technologies, Embedded and Multimedia for Human-centric Computing. Lecture Notes in Electrical Engineering, vol 260. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7262-5_102
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DOI: https://doi.org/10.1007/978-94-007-7262-5_102
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