Related Work
In this chapter, we introduce the general related work for context-aware ranking with factorization models. Related work on specific issues like tag recommenders, Markov chains, etc. is discussed in detail in the corresponding chapters. Here, we discuss three general topics. The first one is recommender systems because the standard task of personalized item recommendation (a two mode problem) can be seen as context-aware ranking where the context is the user. Nevertheless in recommender systems, the term ‘context’ is usually used only for cases with at least three modes and furthermore the first mode is typically assumed to be the user. Thus, in the discussion about recommender systems we stick to the definition within the recommender community and use the term context − aware recommender system only for ranking problems with at least three modes. In contrast to this, in this book we use the term context − aware ranking for any number of modes. Secondly, we investigate factorization models on which our proposed approach is based. Finally, we discuss the literature about ranking in general and context-aware ranking in particular.
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References
Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems 23(1), 103–145 (2005)
Agrawal, R., Rantzau, R., Terzi, E.: Context-sensitive ranking. In: SIGMOD 2006: Proceedings of the 2006 ACM SIGMOD international conference on Management of data, pp. 383–394. ACM, New York (2006)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. In: WWW7: Proceedings of the Seventh International Conference on World Wide Web, vol. 7, pp. 107–117. Elsevier Science Publishers B. V, Amsterdam (1998)
Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., Hullender, G.: Learning to rank using gradient descent. In: ICML 2005: Proceedings of the 22nd International Conference on Machine Learning, pp. 89–96. ACM Press, New York (2005)
Carroll, J., Chang, J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of eckart-young decomposition. Psychometrika 35, 283–319 (1970)
Harshman, R.A.: Foundations of the parafac procedure: models and conditions for an ’exploratory’ multimodal factor analysis. UCLA Working Papers in Phonetics, 1–84 (1970)
Haveliwala, T.H.: Topic-sensitive pagerank: A context-sensitive ranking algorithm for web search. IEEE Transactions on Knowledge and Data Engineering 15(4), 784–796 (2003)
Hofmann, T.: Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. 22(1), 89–115 (2004)
Huang, J., Guestrin, C., Guibas, L.: Efficient inference for distributions on permutations. In: Platt, J., Koller, D., Singer, Y., Roweis, S. (eds.) Advances in Neural Information Processing Systems, vol. 20, pp. 697–704. MIT Press, Cambridge (2008)
Kondor, R., Howard, A., Jebara, T.: Multi-object tracking with representations of the symmetric group. In: Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, San Juan, Puerto Rico (2007)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: KDD 2008: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434. ACM, New York (2008)
Lathauwer, L.D., Moor, B.D., Vandewalle, J.: A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl. 21(4), 1253–1278 (2000)
Oku, K., Nakajima, S., Miyazaki, J., Uemura, S.: Context-aware svm for context-dependent information recommendation. In: MDM 2006: Proceedings of the 7th International Conference on Mobile Data Management, p. 109. IEEE Computer Society, Washington (2006)
Panniello, U., Tuzhilin, A., Gorgoglione, M., Palmisano, C., Pedone, A.: Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems. In: RecSys 2009: Proceedings of the third ACM conference on Recommender systems, pp. 265–268. ACM, New York (2009)
Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: Proceedings of the International Conference on Machine Learning, vol. 25 (2008)
Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted boltzmann machines for collaborative filtering. In: ICML 2007: Proceedings of the 24th International Conference on Machine Learning, pp. 791–798. ACM, New York (2007)
Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295. ACM Press, New York (2001)
Schmidt-Thieme, L.: Compound classification models for recommender systems. In: IEEE International Conference on Data Mining (ICDM 2005), pp. 378–385 (2005)
Srebro, N., Rennie, J.D.M., Jaakola, T.S.: Maximum-margin matrix factorization. In: Advances in Neural Information Processing Systems, vol. 17, pp. 1329–1336. MIT Press, Cambridge (2005)
Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: Tag recommendations based on tensor dimensionality reduction. In: RecSys 2008: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 43–50. ACM, New York (2008)
Tucker, L.: Some mathematical notes on three-mode factor analysis. Psychometrika 31, 279–311 (1966)
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Rendle, S. (2010). Related Work. In: Context-Aware Ranking with Factorization Models. Studies in Computational Intelligence, vol 330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16898-7_2
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