## Abstract

The neighborhood-based methods of the previous chapter can be viewed as generalizations of *k*-nearest neighbor classifiers, which are commonly used in machine learning.

## Bibliography

- [13]D. Agarwal, and B. Chen. Regression-based latent factor models.
*ACM KDD Conference*, pp. 19–28. 2009.Google Scholar - [18]C. Aggarwal. Data classification: algorithms and applications.
*CRC Press*, 2014.Google Scholar - [22]C. Aggarwal. Data mining: the textbook.
*Springer*, New York, 2015.Google Scholar - [23]C. Aggarwal and J. Han. Frequent pattern mining.
*Springer*, New York, 2014.Google Scholar - [24]C. Aggarwal and S. Parthasarathy. Mining massively incomplete data sets by conceptual reconstruction.
*ACM KDD Conference*, pp. 227–232, 2001.Google Scholar - [25]C. Aggarwal, C. Procopiuc, and P. S. Yu. Finding localized associations in market basket data.
*IEEE Transactions on Knowledge and Data Engineering*, 14(1), pp. 51–62, 2001.CrossRefGoogle Scholar - [31]C. Aggarwal, Z. Sun, and P. Yu. Online generation of profile association rules.
*ACM KDD Conference*, pp. 129–133, 1998.Google Scholar - [32]C. Aggarwal, Z. Sun, and P. Yu. Online algorithms for finding profile association rules,
*CIKM Conference*, pp. 86–95, 1998.Google Scholar - [58]R. Battiti. Accelerated backpropagation learning: Two optimization methods.
*Complex Systems*, 3(4), pp. 331–342, 1989.zbMATHGoogle Scholar - [72]R. Bell and Y. Koren. Scalable collaborative filtering with jointly derived neighborhood interpolation weights.
*IEEE International Conference on Data Mining*, pp. 43–52, 2007.Google Scholar - [73]R. Bell and Y. Koren. Lessons from the Netflix prize challenge.
*ACM SIGKDD Explorations Newsletter*, 9(2), pp. 75–79, 2007.CrossRefGoogle Scholar - [76]D. P. Bertsekas. Nonlinear programming.
*Athena Scientific Publishers*, Belmont, 1999.Google Scholar - [82]D. Billsus and M. Pazzani. Learning collaborative information filters.
*ICML Conference*, pp. 46–54, 1998.Google Scholar - [87]C. M. Bishop. Neural networks for pattern recognition.
*Oxford University Press*, 1995.Google Scholar - [96]M. Brand. Fast online SVD revisions for lightweight recommender systems.
*SIAM Conference on Data Mining*, pp. 37–46, 2003.Google Scholar - [127]J. Cai, E. Candes, and Z. Shen. A singular value thresholding algorithm for matrix completion.
*SIAM Journal on Optimization*, 20(4), 1956–1982, 2010.Google Scholar - [133]J. Canny. Collaborative filtering with privacy via factor analysis.
*ACM SIGR Conference*, pp. 238–245, 2002.Google Scholar - [151]T. Chen, Z. Zheng, Q. Lu, W. Zhang, and Y. Yu. Feature-based matrix factorization.
*arXiv preprint*arXiv:1109.2271, 2011.Google Scholar - [161]A. Cichocki and R. Zdunek. Regularized alternating least squares algorithms for non-negative matrix/tensor factorization.
*International Symposium on Neural Networks*, pp. 793–802. 2007.Google Scholar - [180]D. DeCoste. Collaborative prediction using ensembles of maximum margin matrix factorizations.
*International Conference on Machine Learning*, pp. 249–256, 2006.Google Scholar - [184]R. Devooght, N. Kourtellis, and A. Mantrach. Dynamic matrix factorization with priors on unknown values.
*ACM KDD Conference*, 2015.Google Scholar - [217]R. Gemulla, E. Nijkamp, P. Haas, and Y. Sismanis. Large-scale matrix factorization with distributed stochastic gradient descent.
*ACM KDD Conference*, pp. 69–77, 2011.Google Scholar - [219]L. Getoor and M. Sahami. Using probabilistic relational models for collaborative filtering.
*Workshop on Web Usage Analysis and User Profiling*, 1999.Google Scholar - [220]F. Girosi, M. Jones, and T. Poggio. Regularization theory and neural networks architectures.
*Neural Computation*, 2(2), pp. 219–269, 1995.CrossRefGoogle Scholar - [252]T. Hofmann. Latent semantic models for collaborative filtering.
*ACM Transactions on Information Systems (TOIS)*, 22(1), pp. 89–114, 2004.CrossRefGoogle Scholar - [260]Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets.
*IEEE International Conference on Data Mining*, pp. 263–272, 2008.Google Scholar - [267]P. Jain and I. Dhillon. Provable inductive matrix completion.
*arXiv preprint arXiv:1306.0626*http://arxiv.org/abs/1306.0626. - [268]P. Jain, P. Netrapalli, and S. Sanghavi. Low-rank matrix completion using alternating minimization.
*ACM Symposium on Theory of Computing*, pp. 665–674, 2013.Google Scholar - [300]D. Kim, and B. Yum. Collaborative filtering Based on iterative principal component analysis,
*Expert Systems with Applications*, 28, pp. 623–830, 2005.Google Scholar - [301]H. Kim and H. Park. Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method.
*SIAM Journal on Matrix Analysis and Applications*, 30(2), pp. 713–730, 2008.MathSciNetCrossRefzbMATHGoogle Scholar - [309]Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model.
*ACM KDD Conference*, pp. 426–434, 2008. Extended version of this paper appears as: “Y. Koren. Factor in the neighbors: Scalable and accurate collaborative filtering.*ACM Transactions on Knowledge Discovery from Data (TKDD)*, 4(1), 1, 2010.”Google Scholar - [310]Y. Koren. Collaborative filtering with temporal dynamics.
*ACM KDD Conference*, pp. 447–455, 2009. Another version also appears in the*Communications of the ACM,*, 53(4), pp. 89–97, 2010.Google Scholar - [311]Y. Koren. The Bellkor solution to the Netflix grand prize.
*Netflix prize documentation*, 81, 2009. http://www.netflixprize.com/assets/GrandPrize2009_BPC_BellKor.pdf - [312]Y. Koren and R. Bell. Advances in collaborative filtering.
*Recommender Systems Handbook*, Springer, pp. 145–186, 2011. (Extended version in 2015 edition of handbook).Google Scholar - [313]Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems.
*Computer*, 42(8), pp. 30–37, 2009.CrossRefGoogle Scholar - [321]S. Kabbur, X. Ning, and G. Karypis. FISM: factored item similarity models for top-N recommender systems.
*ACM KDD Conference*, pp. 659–667, 2013.Google Scholar - [322]S. Kabbur and G. Karypis. NLMF: NonLinear Matrix Factorization Methods for Top-N Recommender Systems.
*IEEE Data Mining Workshop (ICDMW)*, pp. 167–174, 2014.Google Scholar - [331]A. Langville, C. Meyer, R. Albright, J. Cox, and D. Duling. Initializations for the nonnegative matrix factorization.
*ACM KDD Conference*, pp. 23–26, 2006.Google Scholar - [342]D. Lemire and A. Maclachlan. Slope one predictors for online rating-based collaborative filtering.
*SIAM Conference on Data Mining*, 2005.Google Scholar - [351]M. Li, T. Zhang, Y. Chen, and A. Smola. Efficient mini-batch training for stochastic optimization.
*ACM KDD Conference*, pp. 661–670, 2014.Google Scholar - [357]C.-J. Lin. Projected gradient methods for nonnegative matrix factorization.
*Neural Computation*, 19(10), pp. 2576–2779, 2007.MathSciNetCrossRefGoogle Scholar - [358]W. Lin. Association rule mining for collaborative recommender systems.
*Masters Thesis*, Worcester Polytechnic Institute, 2000.Google Scholar - [359]W. Lin, S. Alvarez, and C. Ruiz. Efficient adaptive-support association rule mining for recommender systems.
*Data Mining and Knowledge Discovery*, 6(1), pp. 83–105, 2002.MathSciNetCrossRefGoogle Scholar - [365]B. Liu, W. Hsu, and Y. Ma. Mining association rules with multiple minimum supports.
*ACM KDD Conference*, pp. 337–341, 1999.Google Scholar - [371]X. Liu, C. Aggarwal, Y.-F. Lee, X. Kong, X. Sun, and S. Sathe. Kernelized matrix factorization for collaborative filtering.
*SIAM Conference on Data Mining*, 2016.Google Scholar - [434]A. Mild and M. Natter. Collaborative filtering or regression models for Internet recommendation systems?.
*Journal of Targeting, Measurement and Analysis for Marketing*, 10(4), pp. 304–313, 2002.CrossRefGoogle Scholar - [437]K. Miyahara, and M. J. Pazzani. Collaborative filtering with the simple Bayesian classifier.
*Pacific Rim International Conference on Artificial Intelligence*, 2000.Google Scholar - [441]B. Mobasher, H. Dai, T. Luo, and M. Nakagawa. Effective personalization based on association rule discovery from Web usage data.
*ACM Workshop on Web Information and Data Management*, pp. 9–15, 2001.Google Scholar - [455]X. Ning and G. Karypis. SLIM: Sparse linear methods for top-N recommender systems.
*IEEE International Conference on Data Mining*, pp. 497–506, 2011.Google Scholar - [457]D. Oard and J. Kim. Implicit feedback for recommender systems.
*Proceedings of the AAAI Workshop on Recommender Systems*, pp. 81–83, 1998.Google Scholar - [460]P. Paatero and U. Tapper. Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values.
*Environmetrics*, 5(2), pp. 111–126, 1994.CrossRefGoogle Scholar - [467]R. Pan, Y. Zhou, B. Cao, N. Liu, R. Lukose, M. Scholz, Q. Yang. One-class collaborative filtering.
*IEEE International Conference on Data Mining*, pp. 502–511, 2008.Google Scholar - [468]R. Pan, and M. Scholz. Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering.
*ACM KDD Conference*, pp. 667–676, 2009.Google Scholar - [472]S. Parthasarathy and C. Aggarwal. On the use of conceptual reconstruction for mining massively incomplete data sets.
*IEEE Transactions on Knowledge and Data Engineering*, 15(6), pp. 1512–1521, 2003.CrossRefGoogle Scholar - [473]A. Paterek. Improving regularized singular value decomposition for collaborative filtering.
*Proceedings of KDD Cup and Workshop*, 2007.Google Scholar - [474]V. Pauca, J. Piper, and R. Plemmons. Nonnegative matrix factorization for spectral data analysis.
*Linear algebra and its applications*, 416(1), pp. 29–47, 2006.MathSciNetCrossRefzbMATHGoogle Scholar - [493]S. Rendle. Factorization machines.
*IEEE International Conference on Data Mining*, pp. 995–100, 2010.Google Scholar - [500]J. Rennie and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction.
*ICML Conference*, pp. 713–718, 2005.Google Scholar - [517]R. Salakhutdinov, and A. Mnih. Probabilistic matrix factorization.
*Advances in Neural and Information Processing Systems*, pp. 1257–1264, 2007.Google Scholar - [518]R. Salakhutdinov, and A. Mnih. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo.
*International Conference on Machine Learning*, pp. 880–887, 2008.Google Scholar - [519]R. Salakhutdinov, A. Mnih, and G. Hinton. Restricted Boltzmann machines for collaborative filtering.
*International conference on Machine Learning*, pp. 791–798, 2007.Google Scholar - [524]B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms.
*World Wide Web Conference*, pp. 285–295, 2001.Google Scholar - [525]B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Application of dimensionality reduction in recommender system – a case study.
*WebKDD Workshop at ACM SIGKDD Conference, 2000*. Also appears at*Technical Report TR-00-043*, University of Minnesota, Minneapolis, 2000. https://wwws.cs.umn.edu/tech_reports_upload/tr2000/00-043.pdf - [537]D. Seung, and L. Lee. Algorithms for non-negative matrix factorization.
*Advances in Neural Information Processing Systems*, 13, pp. 556–562, 2001.Google Scholar - [541]H. Shen and J. Z. Huang. Sparse principal component analysis via regularized low rank matrix approximation.
*Journal of multivariate analysis*. 99(6), pp. 1015–1034, 2008.MathSciNetCrossRefzbMATHGoogle Scholar - [552]M.-L. Shyu, C. Haruechaiyasak, S.-C. Chen, and N. Zhao. Collaborative filtering by mining association rules from user access sequences.
*Workshop on Challenges in Web Information Retrieval and Integration*, pp. 128–135, 2005.Google Scholar - [568]G. Strang. An introduction to linear algebra.
*Wellesley Cambridge Press*, 2009.Google Scholar - [569]N. Srebro, J. Rennie, and T. Jaakkola. Maximum-margin matrix factorization.
*Advances in neural information processing systems*, pp. 1329–1336, 2004.Google Scholar - [571]X. Su, T. Khoshgoftaar, X. Zhu, and R. Greiner. Imputation-boosted collaborative filtering using machine learning classifiers.
*ACM symposium on Applied computing*, pp. 949–950, 2008.Google Scholar - [586]G. Takacs, I. Pilaszy, B. Nemeth, and D. Tikk. Matrix factorization and neighbor based algorithms for the Netflix prize problem.
*ACM Conference on Recommender Systems*, pp. 267–274, 2008.Google Scholar - [620]S. Vucetic and Z. Obradovic. Collaborative filtering using a regression-based approach.
*Knowledge and Information Systems*, 7(1), pp. 1–22, 2005.CrossRefGoogle Scholar - [624]M. Weimer, A. Karatzoglou, Q. Le, and A. Smola. CoFiRank: Maximum margin matrix factorization for collaborative ranking.
*Advances in Neural Information Processing Systems*, 2007.Google Scholar - [629]S. Wild, J. Curry, and A. Dougherty. Improving non-negative matrix factorizations through structured initialization.
*Pattern Recognition*, 37(11), pp. 2217–2232, 2004.CrossRefGoogle Scholar - [638]Z. Xia, Y. Dong, and G. Xing. Support vector machines for collaborative filtering.
*Proceedings of the 44th Annual Southeast Regional Conference*, pp. 169–174, 2006.Google Scholar - [650]H. F. Yu, C. Hsieh, S. Si, and I. S. Dhillon. Scalable coordinate descent approaches to parallel matrix factorization for recommender systems.
*IEEE International Conference on Data Mining*, pp. 765–774, 2012.Google Scholar - [651]K. Yu, S. Zhu, J. Lafferty, and Y. Gong. Fast nonparametric matrix factorization for large-scale collaborative filtering.
*ACM SIGIR Conference*, pp. 211–218, 2009.Google Scholar - [666]S. Zhang, W. Wang, J. Ford, and F. Makedon. Learning from incomplete ratings using nonnegative matrix factorization.
*SIAM Conference on Data Mining*, pp. 549–553, 2006.Google Scholar - [669]T. Zhang and V. Iyengar. Recommender systems using linear classifiers.
*Journal of Machine Learning Research*, 2, pp. 313–334, 2002.zbMATHGoogle Scholar - [676]K. Zhou, S. Yang, and H. Zha. Functional matrix factorizations for cold-start recommendation.
*ACM SIGIR Conference*, pp. 315–324, 2011.Google Scholar - [677]Y. Zhou, D. Wilkinson, R. Schreiber, and R. Pan. Large-scale parallel collaborative filtering for the Netflix prize.
*Algorithmic Aspects in Information and Management*, pp. 337–348, 2008.Google Scholar - [679]C. Ziegler. Applying feed-forward neural networks to collaborative filtering, Master’s Thesis, Universitat Freiburg, 2006.Google Scholar
- [704]

## Copyright information

© Springer International Publishing Switzerland 2016