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Collaborative filtering based on bi-relational data representation

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Widely-referenced approaches to collaborative filtering (CF) are based on the use of an input matrix that represents each user profile as a vector in a space of items and each item as a vector in a space of users. When the behavioral input data have the form of (userX, likes, itemY) and (userX, dislikes, itemY) triples one has to propose a representation of the user feedback data that is more suitable for the use of propositional data than the ordinary user-item ratings matrix. We propose to use an element-fact matrix, in which columns represent RDF-like behavioral data triples and rows represent users, items, and relations. By following such a triple-based approach to the bi-relational behavioral data representation we are able to improve the quality of collaborative filtering. One of the key findings of the research presented in this paper is that the proposed bi-relational behavioral data representation, while combined with reflective matrix processing, significantly outperforms state-of-the-art collaborative filtering methods based on the use of a ‘standard’ user-item matrix.
Rocznik
Strony
67--83
Opis fizyczny
Bibliogr. 26 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] Aggarwal C.C., Wolf J.L., Wu K.-L., Yu P.S., Horting Hatches an Egg: A New Graph-Theoretic Approach to Collaborative Filtering, in: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, 1999, pp. 201-212.
  • [2] Ciesielczyk M., Szwabe A., RSVD-based Dimensionality Reduction for Recommender Systems, International Journal of Machine Learning and Computing, vol. 1, no. 2, 2011, 170-175.
  • [3] 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, 240-256.
  • [4] Cremonesi P., Koren Y., Turrin R., Performance of Recommender Algorithms on Topn Recommendation Tasks, in: Proceedings of the Fourth ACM Conference on Recommender Systems (RecSys ’10), New York, NY, USA, 2010, pp. 39--46.
  • [5] Damljanovic D., Petrak J., Lupu M., Cunningham H., Carlsson M., Engstrom G., Andersson B., Random Indexing for Finding Similar Nodes within Large RDF graphs, in: R. Garcia-Castro, D. Fensel, G. Antoniou (eds.), The Semantic Web: ESWC 2011 Workshops, LNCS, vol. 7117, Springer, Berlin Heidelberg, 2011, 156-171.
  • [6] Fensel D., van Harmelen F., Unifying reasoning and search to web scale, IEEE Internet Computing, 11(2), 96, 2007, 94-95.
  • [7] Fouss F., Francoisse K., Yen L., Pirotte A., Saerens M., An experimental investigation of kernels on graphs for collaborative recommendation and semi-supervised classification, Neural Networks, 31, 2012, 53-72.
  • [8] Franz T., Schultz A., Sizov S., Staab S., Triplerank: Ranking Semantic Web Data by Tensor Decomposition, in: Proceedings of The Semantic Web-ISWC, 2009, pp. 213-228.
  • [9] 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.
  • [10] Li Y., Hu J., Zhai C.X., Chen Y., Improving one-class collaborative filtering by incorporating rich user information, in: Proceedings of the 19th ACM international conference on Information and knowledge management (CIKM ’10), ACM, New York, NY, USA, 2010, pp. 959-968.
  • [11] Manning Ch. D., Raghavan P., Schütze H., Introduction to Information Retrieval, Cambridge University Press, 2008.
  • [12] Nickel M., Tresp V., Kriegel H.P., A Three-Way Model for Collective Learning on Multi-Relational Data, in: Proceedings of the 28th International Conference on Machine Learning, 2011, pp. 809-816.
  • [13] Pan R., Zhou Y., Cao B., Liu N. N., Lukose R., Scholz M., Yang Q.: One-Class Collaborative Filtering. Technical Report. HPL-2008-48R1, HP Laboratories, 2008.
  • [14] Pitowsky I., Quantum Probability, Quantum Logic, Lecture Notes in Physics, 321, Heidelberg, Springer, 1989.
  • [15] Saganowski S., Brodka P., Kazienko P., Influence of the User Importance Measure on the Group Evolution Discovery, Foundations of Computing and Decision Sciences, 2012, 295-305.
  • [16] 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.
  • [17] Sindhwani V., Bucak S.S., Hu J., Mojsilovic A., A Family of Non-negative Matrix Factorizations for One-Class Collaborative Filtering Problems, in: Proceedings of the ACM Recommender Systems Conference, RecSys ’2009, New York, 2009.
  • [18] Singh A.P., Gordon G.J., Relational Learning via Collective Matrix Factorization, in: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 2008, pp. 650-658.
  • [19] Steck H., Training and testing of recommender systems on data missing not at random, in: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD ’10), ACM, New York, NY, USA, 2010, pp. 713-722.
  • [20] Struyf J., Blockeel H., Relational Learning, in: C. Sammut, G.Webb (eds.), Encyclopedia of Machine Learning, Springer, 2010, 851-857.
  • [21] Sutskever I., Salakhutdinov R., Tenenbaum J. B.: Modelling Relational Data Using Bayesian Clustered Tensor Factorization, Advances in Neural Information Processing Systems, 22, 2009.
  • [22] Szwabe A., Ciesielczyk M., Janasiewicz T., Semantically enhanced collaborative filtering based on RSVD, in: P. Jedrzejowicz , N.-T. Nguyen, K. Hoang (eds.), Computational Collective Intelligence, Technologies and Applications, LNCS, vol. 6923, Springer, Berlin Heidelberg, 2011, 10-19.
  • [23] Szwabe A., Misiorek P., Walkowiak P., Reflective Relational Learning for Ontology Alignment, in: S. Omaru et al. (eds.), Distributed Computing and Artificial Intelligence, Advances in Intelligent and Soft Computing, vol. 151, Springer-Verlag Berlin/Heidelberg, 2012, 519-526.
  • [24] 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, 142-151.
  • [25] Todorova P., Kiryakov A., Ognyano D., Peikov I., Velkov R., Tashev Z., Conclusions from Experimental Data and Combinatorics Analysis. LarKC project deliverable 2.7.3, Technical Report, The Large Knowledge Collider (LarKC), 2009.
  • [26] van Rijsbergen C.J.: The geometry of IR, The Geometry of Information Retrieval, Cambridge University Press, New York, USA, 2004, pp. 73-101.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a9634ed8-4732-4049-a108-3fad341d5c7a
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