The research team behind Yusp has already been recognized. Awards include a tied-first place in the Netflix Prize competition, and first places, podium finishes at prestigious Data Mining competitions including ACM KDD cups. The academic record of the team includes 20+ publications with 500+ citations. YUSP is one of the largest third-party recommendations engine providers in the world today, serving over 400 million individually tailored recommendations per day.
Quadrana, Massimo, Alexandros Karatzoglou, Balázs Hidasi, and Paolo Cremonesi. Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks. arXiv preprint arXiv:1706.04148 (2017).
Balázs Hidasi, and Alexandros Karatzoglou. Recurrent Neural Networks with Top-k Gains for Session-based Recommendations. arXiv preprint arXiv:1706.03847 (2017).
Balázs Hidasi, et al. Parallel recurrent neural network architectures for feature-rich session-based recommendations. Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016.
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. (17 February 2016). Session-based recommendations with recurrent neural networks. ICLR 2016,Proceedings of the 4th International Conference on Learning Representations.
Benjamin Kille, Fabian Abel, Balázs Hidasi, and Sahin Albayrak. Using interaction signals for job recommendations. SIREMTI 2015, Workshop on Situation Recognition by Mining Temporal Information, held in conjunction with the 7th International Conference on Mobile Computing, Applications and Services MobiCASE 2015.
Martha Larson, Domonkos Tikk, Roberto Turrin. (20 September 2015). Overview of ACM RecSys CrowdRec 2015 Workshop: Crowdsourcing and Human Computation for Recommender Systems. RecSys 2015, Proceedings of the 9th ACM Conference on Recommender Systems. p 341-342
Balázs Hidasi, Domonkos Tikk. (14 July 2015). Speeding up ALS learning via approximate methods for context-aware recommendations. Knowledge and Information Systems, Springer London. pp 1-25
István Pilászy. (20 September 2015). Neighbor methods vs. matrix factorization: case studies of real-life recommendations. RecSys ’15 Proceedings of the 9th ACM Conference on Recommender Systems.
Balázs Hidasi. (September 2015). Context-aware preference modeling with factorization. RecSys ’15Proceedings of the 9th ACM Conference on Recommender Systems. pp 371-375. Paper/Presentation/Poster
Bottyán Németh. (20 September 2015). Scaling up Recommendation Services in Many Dimensions. RecSys ’15 Proceedings of the 9th ACM Conference on Recommender Systems.
Balázs Hidasi and Domonkos Tikk: General factorization framework for context-aware recommendations, Data Mining and Knowledge Discovery, May 2015.
Gábor Takács and Domonkos Tikk: Alternating least squares for personalized ranking, ACM Recsys 2012: Proceedings of the sixth ACM conference on Recommender systems, 83-90.
Gábor Takács, István Pilászy, Bottyán Németh, and Domonkos Tikk: Scalable collaborative filtering approaches for large recommender systems, Journal of Machine Learning Research, 10: 623-656, 2009.
Balázs Hidasi: Factorization models for context-aware recommendations. Infocommunications Journal VI/4, December 2014.
Alan Said, Domonkos Tikk, and Paolo Cremonesi: Benchmarking – A methodology for ensuring the relative quality of recommendation systems in software engineering.In Recommendation Systems in Software Engineering, Robillard, M.P., Maalej, W., Walker, R.J., Zimmermann, Th. (Eds.), Springer, 2014, ISBN 978-3-642-45135-5.
Alan Said, Martha Larson, Domonkos Tikk, Paolo Cremonesi, Alexandros Karatzoglou, Frank Hopfgartner, Roberto Turrin, and Joost Geurts: User-item reciprocity in recommender systems: Incentivizing the crowd. ProS 2014: Workshop on UMAP Projects Synergy, held in conjunction with 22nd Conference on User Modelling, Adaptation and Personalization. July 7-11, Aalborg, Denmark
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
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
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.
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)
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)
Balázs Hidasi and Domonkos Tikk: Enhancing matrix factorization through initialization for implicit feedback databases,CARR 2012: 2nd Workshop on Context-awareness in Retrieval and Recommendation, pp. 2-9
Dávid Zibriczky, Balázs Hidasi, Zoltán Petres, and Domonkos Tikk: Personalized recommendation of linear content on interactive TV platforms: beating the cold start and noisy implicit user feedback, TVMMP 2012: International Workshop on TV and Multimedia Personalization, held in conjunction with UMAP 2012: 20th conference on User Modeling, Adaptation, and Personalization
Domonkos Tikk: From a toolkit of recommendation algorithms into a real business: the Gravity R&D experience, Workshop of Recommender systems challenge 2012, held in conjunction with ACM Recsys 2012: the sixth ACM conference on Recommender systems
Balázs Hidasi and Domonkos Tikk: Fast ALS-Based tensor factorization for context-aware recommendation from implicit feedback, ECML-PKDD 2012: Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases – Volume Part II, 67-82.
Gábor Takács, István Pilászy, and Domonkos Tikk: Applications of the conjugate gradient method for implicit feedback collaborative filtering, ACM Recsys 2011: Proceedings of the 5th ACM conference on Recommender systems, 297-300
István Pilászy, Dávid Zibriczky, and Domonkos Tikk: Fast ALS-based matrix factorization for explicit and implicit feedback datasets, ACM Recsys 2010: Proceedings of the 4th ACM conference on Recommender systems, 71-78
István Pilászy and Domonkos Tikk: Recommending new movies: even a few ratings are more valuable than metadata, ACM Recsys 2009: Proceedings of the 3rd ACM conference on Recommender systems, 93-100.