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Holistic Entropy Reduction for Collaborative Filtering

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
We propose a collaborative filtering (CF) method that uses behavioral data provided as propositions having the RDF-compliant form of (user X, likes, item Y ) triples. The method involves the application of a novel self-configuration technique for the generation of vector-space representations optimized from the information-theoretic perspective. The method, referred to as Holistic Probabilistic Modus Ponendo Ponens (HPMPP), enables reasoning about the likelihood of unknown facts. The proposed vector-space graph representation model is based on the probabilistic apparatus of quantum Information Retrieval and on the compatibility of all operators representing subjects, predicates, objects and facts. The dual graph-vector representation of the available propositional data enables the entropy-reducing transformation and supports the compositionality of mutually compatible representations. As shown in the experiments presented in the paper, the compositionality of the vector-space representations allows an HPMPP-based recommendation system to identify which of the unknown facts having the triple form (user X, likes, item Y ) are the most likely to be true in a way that is both effective and, in contrast to methods proposed so far, fully automatic.
Rocznik
Strony
209--229
Opis fizyczny
Bibliogr. 25 poz.
Twórcy
autor
  • Institute of Control and Information Engineering, Poznan University of Technology, M. Sklodowskiej-Curie Square 5, 60-965 Poznan, Poland
autor
  • Institute of Control and Information Engineering, Poznan University of Technology, M. Sklodowskiej-Curie Square 5, 60-965 Poznan, Poland
  • Institute of Control and Information Engineering, Poznan University of Technology, M. Sklodowskiej-Curie Square 5, 60-965 Poznan, Poland
autor
  • Institute of Control and Information Engineering, Poznan University of Technology, M. Sklodowskiej-Curie Square 5, 60-965 Poznan, Poland
Bibliografia
  • [1] Adomavicius G., Tuzhilin A., Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, IEEE Trans. on Knowl. and Data Eng., 17, 2005, pp. 734-749.
  • [2] Bruza P., Widdows D., Woods J.W., A quantum logic of down below, in: K. Engesser, D. Gabbay, and D. Lehmann (eds.), Handbook of Quantum Logic, Quantum Structure and Quantum Computation, Elsevier, 2009, pp. 625-660.
  • [3] Ciesielczyk M., Szwabe A., RI-based dimensionality reduction for recommender systems, in: Proc. of 3rd International Conference on Machine Learning and Computing, IEEE Press, Singapore, 2011.
  • [4] Cohen T., Schaneveldt R., Widdows D., Reflective random indexing and indirect inference: A scalable method for discovery of implicit connections, Journal of Biomedical Informatics, 43, 2, 2010, pp. 240-256.
  • [5] Cremonesi P., Koren Y., Turrin R., Performance of recommender algorithms on top-n recommendation tasks, in: Proceedings of the Fourth ACM Conference on Recommender Systems (RecSys ’10), New York, NY, USA, 2010, pp. 39--46.
  • [6] de Vries A. P., Roelleke T., Relevance information: A loss of entropy but a gain for idf?, in: Proc. of SIGIR, 2005, pp. 282-289.
  • [7] Herlocker J.L., Konstan J.A., Terveen L.G., Riedl J.T., Evaluating collaborative filtering recommender systems, ACM Trans. Information Systems, vol. 22, no. 1, 2004, 5-53.
  • [8] Levin D. A., Peres Y., Wilmer E. L., Markov Chains and Mixing Times, Amer. Math. Soc. Press, Providence, Rhode Island, 2008.
  • [9] Nickel M., Tresp V., Kriegel H.P., A three-way model for collective learning on multirelational data, in: Proceedings of the 28th International Conference on Machine Learning, 2011, pp. 809-816.
  • [10] Nielsen M. A., Chuang I.L., Quantum Computation and Quantum Information, Cambridge University Press, Cambridge, UK, 2010.
  • [11] Pitowsky I., Quantum probability, quantum logic, in: Lecture Notes in Physics, 321, Heidelberg, Springer, 1989.
  • [12] Ricci F., Rokach L., Shapira B., Kantor P.B., Recommender Systems Handbook, Springer, 2011.
  • [13] Sarwar B., Karypis G., Konstan J., Riedl J., Application of dimensionality reduction in recommender system - A case study, in: Proceedings of the ACM EC’00 Conference, Minneapolis, 2000, pp. 158-167.
  • [14] Struyf J., Blockeel H., Relational Learning, in: C. Sammut, G.Webb (eds.), Encyclopedia of Machine Learning, Springer, 2010, pp. 851-857.
  • [15] Sutskever I., Salakhutdinov R., Tenenbaum J. B., Modelling relational data using Bayesian clustered tensor factorization, Advances in Neural Information Processing Systems, 22, 2009.
  • [16] Szwabe A., Ciesielczyk M., Misiorek P., Long-tail recommendation based on Reflective Indexing, in: D. Wang, M. Reynolds (eds.), AI 2011: Advances in Artificial Intelligence, LNCS/LNAI, vol. 7106, Springer, Berlin/Heidelberg, 2011, pp. 142-151.
  • [17] Szwabe A., Misiorek P., Ciesielczyk M., Jedrzejek C., Collaborative filtering based on bi-relational data representation, Foundations of Computing and Decision Sciences, 38, 4, 2013, pp. 67--83.
  • [18] van Rijsbergen C. J., Information Retrieval (2nd ed.), Butterworth-Heinemann, USA, 1979.
  • [19] van Rijsbergen C. J., The Geometry of Information Retrieval, Cambridge University Press, New York, USA, 2004, pp. 73-101.
  • [20] Wilson R.C., Hancock E.R., Luo B., Pattern vectors from algebraic graph theory, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 7, 2005, pp. 1112-1124.
  • [21] Wermser H., Rettinger A., Tresp V., Modeling and learning context-aware recommendation scenarios using tensor decomposition, in: Proceedings of 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2011, pp. 137-144.
  • [22] Xu Z., Kersting K., Tresp V., Multi-relational learning with Gaussian processes, in: Proc. of the 21st International Joint Conference on Artificial Intelligence, San Francisco, CA, USA, 2009, pp. 1309-1314.
  • [23] Yahyaei S., Monz Ch., Applying maximum entropy to known-item email retrieval, in: C. MacDonald, I. Ounis, V. Plachouras, I. Ruthven, R.W. White (eds.), Proc. of European Conference on Information Retrieval (ECIR’08), Springer-Verlag, Berlin Heidelberg, 2008, pp. 406-413.
  • [24] Zitnick C.L., Kanade T., Maximum entropy for collaborative filtering. In: Proc. of the 20th Conf. on Uncertainty in Artificial Intelligence, AUAI Press, Arlington, VA, USA, 2004, pp. 636-643.
  • [25] http://www.grouplens.org
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-15aa6b2c-fa23-4f73-934e-f5599e2cd24f
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