Abstract
In this paper, the psychometrics model, i.e. the rating scale model, is extended from one dimension to multiple dimension. Then, based on this, a novel collaborative filtering algorithm is proposed. In this algorithm, user’s interest and item’s quality are represented by vectors. User’s rating for an item is a weighted summation of the user’s latent ratings for the item in all dimensions, in which the weights are user-specific. Moreover, user’s latent rating in each dimension is assumed to follow a multinomial distribution that is determined by the user’s interest value, the item’s quality value in this dimension, and the thresholds between two consecutive ratings. The parameters are estimated by minimizing the loss function using the stochastic gradient descent method. Experimental results on the benchmark data sets show the superiority of our algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hu, B., Li, Z., Wang, J.: User’s latent interest-based collaborative filtering. In: Gurrin, C., He, Y., Kazai, G., Kruschwitz, U., Little, S., Roelleke, T., Rüger, S., van Rijsbergen, K. (eds.) ECIR 2010. LNCS, vol. 5993, pp. 619–622. Springer, Heidelberg (2010)
Hu, B., Li, Z., Chao, W., et al.: User preference representation based on psychometric models. In: Proc. of 22nd Australasian Database Conference (2011)
Linacre, J.M.: WINSTEPS: Rasch measurement computer program, Chicago (2007), Winsteps.com
Rasch, G.: Probabilistic models for some intelligence and attainment tests. Institute of Educational Research, Copenhagen (1960)
Andrich, D.: A rating formulation for orderedresponse categories. Psychometrikia 43, 561–573 (1978)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proc. of KDD (2008)
Li, B., Yang, Q., Xue, X.: Can movies and books collaborate?—Cross-domain collaborative filtering for sparsity reduction. In: Proc. of IJCAI, pp. 2052–2057 (2009)
Chen, G., Wang, F., Zhang, C.: Collaborative filtering using orthogonal nonnegative matrix tri-factorization. Information Processing & Management 45(3), 368–379 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, H., Zhang, X., Li, Z., Liu, C. (2013). Collaborative Filtering Using Multidimensional Psychometrics Model. In: Wang, J., Xiong, H., Ishikawa, Y., Xu, J., Zhou, J. (eds) Web-Age Information Management. WAIM 2013. Lecture Notes in Computer Science, vol 7923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38562-9_70
Download citation
DOI: https://doi.org/10.1007/978-3-642-38562-9_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38561-2
Online ISBN: 978-3-642-38562-9
eBook Packages: Computer ScienceComputer Science (R0)