Improving Personal Product Recommendation via Friendships’ Expansion

Chunxia Yin, Tao Chu

The First Aircraft Institute, Aviation Industry Corporation of China, Xi’an, China..

DOI: 10.4236/jcc.2013.15001                

Abstract

The trust as a social relationship captures similarity of tastes or interests in perspective. However, the existent trust information is usually very sparse, which may suppress the accuracy of our personal product recommendation algorithm via a listening and trust preference network. Based on this thinking, we experiment the typical trust inference methods to find out the most excellent friend-recommending index which is used to expand the current trust network. Experimental results demonstrate the expanded friendships via superposed random walk can indeed improve the accuracy of our personal product recommendation.

Keywords

Personal Product Recommendation; Trust Inference; Listening and Trust Preference Network

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	C. X. Yin and T. Chu, “Personal Artist Recommendation via a Listening and Trust Preference Network,” Physica A, Vol. 391, No. 5, 2012, pp. 1991-1999. http://dx.doi.org/10.1016/j.physa.2011.11.054
[2]	J. Golbeck, “Trust on the World Wide Web: A Survey,” Foundations and Trends in Web Science, Vol. 1, No. 2, 2006, pp. 131-197. http://dx.doi.org/10.1561/1800000006
[3]	R. Albert and A.-L. Barabasi, “Statistical Mechanics of Complex Networks,” Reviews of Modern Physics, Vol. 74, No. 1, 2002, pp. 47-97. http://dx.doi.org/10.1103/RevModPhys.74.47
[4]	S. N. Dorogovtsev and J. F. F. Mendes, “Evolution of Networks,” Advances in Physics, Vol. 51, No. 4, 2002, pp. 1079-1187. http://dx.doi.org/10.1080/00018730110112519
[5]	S. Boccaletti, V. Latora, Y. Moreno, M. Chavez and D.-U. Hwang, “Complex Networks: Structure and Dynamics,” Physics Reports, Vol. 424, No. 4, 2006, pp. 175-308. http://dx.doi.org/10.1016/j.physrep.2005.10.009
[6]	L. da F. Costa, F. A. Rodrigues, G. Travieso and P. R. U. Boas, “Characterization of Complex Networks: A Survey of Measurements,” Advances in Physics, Vol. 56, No. 1, 2007, pp. 167-242. http://dx.doi.org/10.1080/00018730601170527
[7]	D. Liben-Nowell and J. Kleinberg, “The Link-Prediction Problem for Social Networks,” Journal of the American Society for Information Science and Technology, Vol. 58, No. 7, 2007, pp. 1019-1031. http://dx.doi.org/10.1002/asi.20591
[8]	J. A. Hanely and B. J. McNeil, “The Meaning and Use of the Area under a Receiver Operating Characteristic (ROC) Curve,” Radiology, Vol. 143, 1982, pp. 29-36.
[9]	J. L. Herlocker, J. A. Konstan, L. G. Terveen and J. T. Riedl, “Evaluating Collaborative Filtering Recommender Systems,” ACM Transactions on Information Systems, Vol. 22, No. 1, 2004, pp. 5-53. http://dx.doi.org/10.1145/963770.963772
[10]	A. Broder, R. Kumar, F. Maghoul, P. Raghavan, S. Ra- jagopalan, R. Stata, A. Tomkins and J. Wiener, “Graph Structure in the Web,” Computer Networks, Vol. 33, No. 1, 2000, pp. 309-320. http://dx.doi.org/10.1016/S1389-1286(00)00083-9
[11]	J. A. Konstan, B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon and J. Riedl, “GroupLens: Applying Collaborative Filtering to Usenet News,” Communications of the ACM, Vol. 40, No. 3, 1997, pp. 77-87. http://dx.doi.org/10.1145/245108.245126
[12]	L. Lü and T. Zhou, “Link Prediction in Complex Networks: A Survey,” Physica A, Vol. 390, No. 6, 2011, pp. 1150-1170. http://dx.doi.org/10.1016/j.physa.2010.11.027
[13]	G. Kossinets, “Effects of Missing Data in Social Networks,” Social Networks, Vol. 28, No. 3, 2006, pp. 247-268. http://dx.doi.org/10.1016/j.socnet.2005.07.002
[14]	M. E. J. Newman, “Clustering and Preferential Attachment in Growing Networks,” Physical Review E, Vol. 64, No. 2, 2001, pp. 1-13. http://dx.doi.org/10.1103/PhysRevE.64.025102
[15]	P. Jaccard, “étude Comparative de la Distribution Florale Dans Une Portion desAlpes et des Jura,” Bulletin de la Societe Vaudoise des Science Naturelles, Vol. 37, 1901, pp. 547-579.
[16]	G. Salton and M. J. McGill, “Introduction to Modern Information Retrieval,” MuGraw-Hill, Auckland, 1983.
[17]	E. A. Leicht, P. Holme and M. E. J. Newman, “Vertex Similarity in Networks,” Physical Review E, Vol. 73, No. 2, 2006. http://dx.doi.org/10.1103/PhysRevE.73.026120
[18]	E. Ravasz, A. L. Somera, D. A. Mongru, Z. N. Oltvai and A.-L. Barabási, “Hierarchical Organization of Modularity in Metabolic Networks,” Science, Vol. 297, No. 5586, 2002, pp. 1551-1555. http://dx.doi.org/10.1126/science.1073374
[19]	T. Sørensen, “A Method of Establishing Groups of Equal Amplitude in Plant Sociology Based on Similarity of Species Content and Its Application to Analyses of the Vegetation on Danish Commons,” Biological Skr, Vol. 5, No. 4, 1948, pp. 1-34.
[20]	A.-L. Barabási and R. Albert, “Emergence of Scaling in Random Networks,” Science, Vol. 286, No. 5439, 1999, pp. 509-512. http://dx.doi.org/10.1126/science.286.5439.509
[21]	L. A. Adamic and E. Adar, “Friends and Neighbors on the Web,” Social Networks, Vol. 25, No. 3, 2003, pp. 211- 230. http://dx.doi.org/10.1016/S0378-8733(03)00009-1
[22]	T. Zhou, L. Lü and Y.-C. Zhang, “Predicting Missing Links via Local Information,” European Physical Journal B, Vol. 71, No. 4, 2009, pp. 623-630. http://dx.doi.org/10.1140/epjb/e2009-00335-8
[23]	W. Liu and L. Lü, “Link Prediction Based on Local Random Walk,” EPL, Vol. 89, No. 5, 2010. http://dx.doi.org/10.1209/0295-5075/89/58007
[24]	L. Katz, “A New Status Index Derived from Sociometric Analysis,” Psychmetrika, Vol. 18, No. 1, 1953, pp. 39-43. http://dx.doi.org/10.1007/BF02289026
[25]	P. Chebotarev and E. V. Shamis, “The Matrix-Forest Theorem and Measuring Relations in Small Social Groups,” Automation and Remote Control, Vol. 58, No. 9, 1997, pp. 1505-1514.
[26]	S. Zhou and R. J. Mondragón, “Accurately Modeling the Internet Topology,” Physical Review E, Vol. 70, No. 6, 2004. http://dx.doi.org/10.1103/PhysRevE.70.066108
[27]	T. Zhou, J. Ren, M. Medo and Y.C. Zhang, “Bipartite Network Projection and Personal Recommendation,” Physical Review E, Vol. 76, No. 4, 2007. http://dx.doi.org/10.1103/PhysRevE.76.046115