Publications

Our data mining team publishes actively in the field of recommender systems. Here you can find details for some selected papers along with a brief description on the paper content.

List of publications:

  1. Dávid Zibriczky, Zoltán Petres, Márton Waszlavik and Domonkos Tikk: EPG content recommendation in large scale: a case study on interactive TV platform. Machine Learning with Multimedia Data – Special session at the 12th IEEE International Conference on Machine Learning and Applications (ICMLA'13)
  2. Martha Larson, Alan Said, Yue Shi, Paolo Cremonesi, Domonkos Tikk and A Karatzoglou: Activating the Crowd: exploiting user-item reciprocity for recommendation, CrowdRec 2013: Workshop on Crowdsourcing and Human Computation for Recommender Systems, held in conjunction with 7th ACM Conference on Recommender Systems (Recsys'13).
  3. Alejandro Bellogín, Pablo Castells, Alan Said and Domonkos Tikk: Workshop on Reproducibility and Replication in Recommender Systems Evaluation – RepSys. Recsys 2013: 7th ACM Conference on Recommender Systems, Hong Kong, pp 485-486.
  4. Balázs Hidasi and Domonkos Tikk: Initializing Matrix Factorization Methods on Implicit Feedback Databases, Journal of Universal Computer Science, Volume 19, Issue 12, Pages 1834-1853.
  5. Bottyán Németh, Gábor Takács, István Pilászy and Domonkos Tikk: Visualization of movie features in collaborative filtering, Proc. of the 12th IEEE Int. Conf. on Intelligent Software Methodologies, Tools and Techniques (SoMeT 2013), pp. 229–233
  6. Balázs Hidasi and Domonkos Tikk: Context-aware item-to-item recommendation within the factorization framework, CARR 2013: 3rd Workshop on Context-awareness in Retrieval and Recommendation, held in conjunction with 6th ACM Int. Conf. on Web Search and Data Mining.
  7. B. Hidasi, D. Tikk, Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback, Accepted at: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2012), Bristol, UK, 2012. Read summary >>
  8. G. Takács, D. Tikk, Alternating least squares for personalized ranking, Accepted at: 6th ACM Conf. on Recommender Systems (RecSys 2012), Dublin, Ireland, 2012. Read summary >>
  9. A. Said, D. Tikk, A. Hotho, The Challenge of Recommender System Challenges, Tutorial. Accepted at: 6th ACM Conf. on Recommender Systems (RecSys 2012), Dublin, Ireland, 2012.
  10. A. Said, D. Tikk, K. Stumpf, Y. Shi, M. Larson, P. Cremonesi, Recommender Systems Evaluation: A 3D Benchmark, Accepted at: Workshop on Recommendation Utility Evaluation: Beyond RMSE (RUE 2012), Dublin, Ireland, 2012. 
  11. D. Zibriczky, B. Hidasi, Z. Petres, D. Tikk, Personalized recommendation of linear content on interactive TV platforms: beating the cold start and noisy implicit user feedback, Accepted at: International Workshop on TV and Multimedia Personalization (TVMMP), Montréal, Canada, 2012.
  12. B. Hidasi, D. Tikk, Enhancing matrix factorization through initialization for implicit feedback databases, Proc. of 2nd Workshop on Context-awareness in Retrieval and Recommendation (CaRR 2012), pp. 2-9, Lisbon, Portugal, 2012. Read summary >>
  13. G. Takács, I. Pilászy, D. Tikk, Applications of the conjugate gradient method for implicit feedback collaborative filtering, Proc. of 5th ACM Conf. on Recommender Systems (RecSys 2011), Chicago, IL, USA, 2011. Read summary >>
  14. I. Pilászy, D. Zibriczky, D. Tikk, Fast ALS-based matrix factorization for explicit and implicit feedback datasets, Proc. of 4th ACM Conf. on Recommender Systems (RecSys 2010), pp. 71-78, Barcelona, Spain, 2010. Read summary >>
  15. I. Pilászy, D. Tikk, Recommending new movies: even a few ratings are more valuable than metadata, Proc. of 3th ACM Conf. on Recommender Systems (RecSys 2009), pp. 93-100, New York, NY, USA, 2009. Read summary >>
  16. I. Pilászy, D. Tikk, Explaining recommendations of factorization-based collaborative filtering algorithms, Acta Technica Jaurinensis, 2(2): 233–248, 2009. Read summary >>
  17. G. Takács, I. Pilászy, B. Németh, D. Tikk, Scalable collaborative filtering approaches for large recommender systems, Journal of Machine Learning Research, 10: 623-656, 2009. Read summary >>
  18. I. Pilászy, D. Tikk, Computational complexity reduction for factorization-based collaborative filtering algorithms, Proc. of 10th Conf. on E-Commerce and Web Technologies (EC-Web 2009), pp. 229-239, Linz, Austria, 2009. Read summary >>
  19. G. Takács, I. Pilászy, B. Németh, D. Tikk, Matrix factorization and neighbor based algorithms for the Netflix Prize problem, Proc. of 2nd ACM Conf. on Recommender Systems (RecSys 2008), pp. 267-274, Lausanne, Switzerland, 2008. Read summary >>
  20. G. Takács, I. Pilászy, B. Németh, D. Tikk, A unified approach of factor models and neighbor based methods for large recommender systems, Proc. of 1st IEEE Int. Conf. on the Application of Digital Information and Web Technologies (ICADIWT 2008), pp. 186-191, Ostrava, Czech Republic, 2008. Read summary >>
  21. G. Takács, I. Pilászy, B. Németh, D. Tikk, Investigation of various matrix factorization methods for large recommender systems, Proc. of 2nd Netflix-KDD Workshop at SIGKDD 2008, 14th ACM Int. Conf. on Knowledge Discovery and Data Mining, pp. 21-28, Las Vegas, NV, USA, 2008. Read summary >>
  22. G. Takács, I. Pilászy, B. Németh, D. Tikk, Major components of the Gravity Recommendation System, ACM SIGKDD Explorations Newsletter, 9:80-83, 2007. Read summary >>
  23. G. Takács, I. Pilászy, B. Németh, D. Tikk, On the Gravity Recommendation System, Proc. of KDD Cup Workshop at SIGKDD 2007, 13th ACM Int. Conf. on Knowledge Discovery and Data Mining, pp. 22-30, San Jose, CA, USA, 2007. Read summary >>