An optimization model for collaborative recommendation using a covariance-based regularizer

• 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 Knowl Data Eng 17(6):734–749

  Article  Google Scholar 

• Agarwal D, Chen BC, Elango P, Wang X (2011) Click shaping to optimize multiple objectives. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining (KDD’11), pp 132–140

• Armstrong J (2001) Principles of forecasting, a handbook for researchers and practitioners. Kluwer Academic, Norwell

  Book  Google Scholar 

• Bennett J, Lanning S (2007) The Netflix prize. In: KDD Cup and Workshop in conjunction with KDD

• Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge

  Book  MATH  Google Scholar 

• Cremonesi P, Koren Y, Turrin R (2010) Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the fourth ACM conference on recommender systems (RecSys ’10), pp 39–46

• Devooght R, Kourtellis N, Mantrach A (2015) Dynamic matrix factorization with priors on unknown values. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 189–198

• Deza M, Deza E (2014) Encyclopedia of distances, 3rd edn. Springer, Heidelberg

  MATH  Google Scholar 

• Feldman S (2012) Multi-task averaging: theory and practice. PhD thesis, University of Washington

• Fouss F, Francoisse K, Yen L, Pirotte A, Saerens M (2012) An experimental investigation of kernels on graphs for collaborative recommendation and semi-supervised classification. Neural Netw 31:53–72

  Article  MATH  Google Scholar 

• Fouss F, Saerens M, Shimbo M (2016) Algorithms and models for network data and link analysis. Cambridge University Press, Cambridge

  Book  Google Scholar 

• Gantner Z, Rendle S, Freudenthaler C, Schmidt-Thieme L (2011) MyMediaLite: a free recommender system library. In: Proceedings of the 5th ACM conference on recommender systems (RecSys 2011)

• Gill PE, Murray W, Wright MH (1981) Practical optimization. Academic Press, London

  MATH  Google Scholar 

• Gillis N (2011) Nonnegative matrix factorization: complexity, algorithms and applications. PhD thesis, Université catholique de Louvain

• Gillis N (2014) The why and how of nonnegative matrix factorization. In: Suykens JAK, Signoretto M, Argyriou A (eds) Regularization, optimization, kernels, and support vector machines. Chapman & Hall/CRC, Machine Learning and Pattern Recognition Series, pp 257–291

  Google Scholar 

• Gillis N, Glineur F (2012) Accelerated multiplicative updates and hierarchical Als algorithms for nonnegative matrix factorization. Neural Comput 24(4):1085–1105

  Article  MathSciNet  Google Scholar 

• Gu Q, Zhou J, Ding C (2010) Collaborative filtering: weighted nonnegative matrix factorization incorporating user and item graphs. In: Proceedings of the 2010 SIAM international conference on data mining, pp 199–210

• Guo G, Zhang J, Yorke-Smith N (2013) A novel bayesian similarity measure for recommender systems. In: Proceedings of the 23rd international joint conference on artificial intelligence (IJCAI), pp 2619–2625

• Herlocker J, Konstan J, Riedl J (2002) An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf Retr 5:287–310

  Article  Google Scholar 

• Hill W, Stead L, Rosenstein M, Furnas G (1995) Recommending and evaluating choices in a virtual community of use. In: Proceedings of ACM CHI’95 conference on human factors in computing systems, pp 194–201

• Hsieh CJ, Olsen P (2014) Nuclear norm minimization via active subspace selection. In: Proceedings of the 31st international conference on machine learning (ICML-14), pp 575–583

• Hu Y, Koren Y, Volinsky C (2008) Collaborative filtering for implicit feedback datasets. In: Proceedings of the Eighth IEEE international conference on data mining (ICDM), pp 263–272

• Jambor T, Wang J (2010) Optimizing multiple objectives in collaborative filtering. In: Proceedings of the fourth ACM conference on recommender systems (RecSys ’10), pp 55–62

• Järvelin K, Kekäläinen J (2002) Cumulated gain-based evaluation of ir techniques. ACM Trans Inf Syst 20(4):422–446

  Article  Google Scholar 

• Johnson R, Wichern D (2002) Applied multivariate statistical analysis, 5th edn. Prentice Hall, Englewood Cliffs

  MATH  Google Scholar 

• Kannan R, Ishteva M, Park H (2012) Bounded matrix low rank approximation. In: Proceedings of the 12th IEEE international conference on data mining (ICDM), pp 319–328

• Kim D, Yum BJ (2005) Collaborative filtering based on iterative principal component analysis. Expert Syst Appl 28(4):823–830

  Article  Google Scholar 

• Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (KDD’08), pp 426–434

• Koren Y (2009) Collaborative filtering with temporal dynamics. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining (KDD ’09), pp 447–456

• Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37

  Article  Google Scholar 

• Lilien G, Smith B, Moorthy K (1992) Marketing models. Prentice Hall, Englewood Cliffs

  Google Scholar 

• Lü L, Medo M, Yeung CH, Zhang YC, Zhang ZK, Zhou T (2012) Recommender systems. Phys Rep 519:1–49

  Article  Google Scholar 

• Luo X, Zhou M, Xia Y, Zhu Q (2014) An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems. IEEE Trans Ind Inf 10(2):1273–1284

  Article  Google Scholar 

• Murthi B, Sarkar S (2003) The role of the management sciences in research on personalization. Manag Sci 49(10):1344–1362

  Article  Google Scholar 

• Ning X, Karypis G (2011) SLIM: sparse linear methods for top-n recommender systems. In: Proceedings of the IEEE international conference on data mining (ICDM), pp 497–506

• Ning X, Desrosiers C, Karypis G (2015) A comprehensive survey of neighborhood-based recommendation methods. Springer, Heidelberg, pp 37–76

  Google Scholar 

• Paterek A (2007) Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD cup and workshop, pp 39–42

• Rao N, Yu H-F, Ravikumar P, Dhillon IS (2015) Collaborative filtering with graph information: consistency and scalable methods. In: Cortes C, Lee DD, Sugiyama M, Garnett R (eds) Proceedings of the 28th international conference on neural information processing systems, vol 2, MIT Press, Cambridge, MA, pp 2107–2115

• Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence (AUAI), pp 452–461

• Resnick P, Neophytos I, Mitesh S, Bergstrom P, Riedl J (1994) GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the conference on computer supported cooperative work, pp 175–186

• Rich E (1979) User modeling via stereotypes. Cogn Sci 3(4):329–354

  Article  Google Scholar 

• Rodriguez M, Posse C, Zhang E (2012) Multiple objective optimization in recommender systems. In: Proceedings of the Sixth ACM conference on recommender systems (RecSys ’12), pp 11–18

• Salakhutdinov R, Mnih A (2008) Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: Proceedings of the 25th international conference on machine learning (ICML ’08), pp 880–887

• Salton G (1989) Automatic text processing. Addison-Wesley, Reading

  Google Scholar 

• Shan H, Banerjee A (2010) Generalized probabilistic matrix factorizations for collaborative filtering. In: Proceedings of the 10th IEEE international conference on data mining (ICDM), pp 1025–1030

• Shani G, Gunawardana A (2011) Evaluating recommendation systems. In: Ricci F, Rokach L, Shapira B, Kantor P (eds) Recommender systems handbook. Springer, Boston, MA, pp 257–297

  Chapter  Google Scholar 

• Shardanand U, Maes P (1995) Social information filtering: algorithms for automating ’word of mouth’. In: Proceedings of the conference on human factors in computing systems, pp 210–217

• Smola AJ, Kondor R (2003) Kernels and regularization on graphs. Learning theory and kernel machines, vol 2777. Lecture notes in computer science. Springer, Berlin, pp 144–158

  MATH  Google Scholar 

• Tang J, Gao H, Liu H (2012) mTrust: discerning multi-faceted trust in a connected world. In: Proceedings of the fifth ACM international conference on web search and data mining, pp 93–102

• Yu K, Zhu S, Lafferty J, Gong Y (2009) Fast nonparametric matrix factorization for large-scale collaborative filtering. In: Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval (SIGIR ’09), pp 211–218

• Zhang S, Wang W, Ford J, Makedon F (2006) Learning from incomplete ratings using non-negative matrix factorization. In: Proceedings of the 2006 SIAM international conference on data mining, pp 549–553

• Zhang Y, Chen W, Yin Z (2013) Collaborative filtering with social regularization for TV program recommendation. Knowl Based Syst 54:310–317

  Article  Google Scholar 

Download references