Collaborative Filtering Based on Bi-Relational Data Representation

Szwabe, Andrzej; Misiorek, Pawel; Ciesielczyk, Michal; Jedrzejek, Czeslaw

doi:10.2478/v10209-011-0021-x

Abstract

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.

References

[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. 201-212.10.1145/312129.312230
Search in Google Scholar
[2] Ciesielczyk M., Szwabe A., RSVD-based Dimensionality Reduction for Recommender Systems, International Journal of Machine Learning and Computing, vol. 1, no. 2, 2011, 170-175.10.7763/IJMLC.2011.V1.25
Search in Google Scholar
[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, 240-256.10.1016/j.jbi.2009.09.00319761870
Search in Google Scholar
[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--46.10.1145/1864708.1864721
Search in Google Scholar
[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.
Search in Google Scholar
[6] Fensel D., van Harmelen F., Unifying reasoning and search to web scale, IEEE Internet Computing, 11(2), 96, 2007, 94-95.10.1109/MIC.2007.51
Search in Google Scholar
[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, 53-72.10.1016/j.neunet.2012.03.00122497802
Search in Google Scholar
[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. 213-228.10.1007/978-3-642-04930-9_14
Search in Google Scholar
[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, 5-53.10.1145/963770.963772
Search in Google Scholar
[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. 959-968.10.1145/1871437.1871559
Search in Google Scholar
[11] Manning Ch. D., Raghavan P., Sch¨utze H., Introduction to Information Retrieval, Cambridge University Press, 2008.10.1017/CBO9780511809071
Search in Google Scholar
[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.
Search in Google Scholar
[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, 2008.10.1109/ICDM.2008.16
Search in Google Scholar
[14] Pitowsky I., Quantum Probability, Quantum Logic, Lecture Notes in Physics, 321, Heidelberg, Springer, 1989.
Search in Google Scholar
[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, 295-305.10.2478/v10209-011-0017-6
Search in Google Scholar
[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. 158-167.10.21236/ADA439541
Search in Google Scholar
[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.
Search in Google Scholar
[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. 650-658.10.1145/1401890.1401969
Search in Google Scholar
[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. 713-722.10.1145/1835804.1835895
Search in Google Scholar
[20] Struyf J., Blockeel H., Relational Learning, in: C. Sammut, G.Webb (eds.), Encyclopedia of Machine Learning, Springer, 2010, 851-857.10.1007/978-0-387-30164-8_719
Search in Google Scholar
[21] Sutskever I., Salakhutdinov R., Tenenbaum J. B.: Modelling Relational Data Using Bayesian Clustered Tensor Factorization, Advances in Neural Information Processing Systems, 22, 2009.
Search in Google Scholar
[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.
Search in Google Scholar
[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, 519-526.10.1007/978-3-642-28765-7_62
Search in Google Scholar
[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.
Search in Google Scholar
[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.
Search in Google Scholar
[26] van Rijsbergen C.J.: The geometry of IR, The Geometry of Information Retrieval, Cambridge University Press, New York, USA, 2004, pp. 73-101. 10.1017/CBO9780511543333
Search in Google Scholar

Collaborative Filtering Based on Bi-Relational Data Representation

Abstract

Paradigm

My account