Zeno Gantner
Steffen Rendle
Lars Schmidt-Thieme
ABSTRACT
MyMediaLite is a fast and scalable, multi-purpose library of recommender system algorithms, aimed both at recommender system researchers and practitioners. It addresses two common scenarios in collaborative filtering: rating prediction (e.g. on a scale of 1 to 5 stars) and item prediction from positive-only implicit feedback (e.g. from clicks or purchase actions). The library offers state-of-the-art algorithms for those two tasks. Programs that expose most of the library's functionality, plus a GUI demo, are included in the package. Efficient data structures and a common API are used by the implemented algorithms, and may be used to implement further algorithms. The API also contains methods for real-time updates and loading/storing of already trained recommender models.
MyMediaLite is free/open source software, distributed under the terms of the GNU General Public License (GPL). Its methods have been used in four different industrial field trials of the MyMedia project, including one trial involving over 50,000 households.
- X. Amatriain, M. Torrens, P. Resnick, and M. Zanker, editors. Proceedings of the 2010 ACM Conference on Recommender Systems, (RecSys 2010). ACM, 2010. Google Scholar
- T. Angermann. Empirische Analyse von Open-Source-Empfehlungssystemen. Master's thesis, University of Hildesheim, 2010.Google Scholar
- D. Billsus, M. J. Pazzani, and J. Chen. A learning agent for wireless news access. In Intelligent User Interfaces, pages 33--36, 2000. Google ScholarDigital Library
- D. Bollen, B. P. Knijnenburg, M. C. Willemsen, and M. Graus. Understanding choice overload in recommender systems. In Amatriain et al. {1}. Google ScholarDigital Library
- Z. Gantner, L. Drumond, C. Freudenthaler, S. Rendle, and L. Schmidt-Thieme. Learning attribute-to-feature mappings for cold-start recommendations. In G. I. Webb, B. Liu, C. Zhang, D. Gunopulos, and X. Wu, editors, ICDM 2010. IEEE Computer Society, 2010. Google ScholarDigital Library
- Z. Gantner, S. Rendle, and L. Schmidt-Thieme. Factorization models for context-/time-aware movie recommendations. In Challenge on Context-aware Movie Recommendation (CAMRa2010). ACM, 2010. Google ScholarDigital Library
- K. Goldberg, T. Roeder, D. Gupta, and C. Perkins. Eigentaste: A constant time collaborative filtering algorithm, 2000.Google Scholar
- J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In SIGIR 1999, New York, NY, USA, 1999. ACM. Google ScholarDigital Library
- J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst., 22:5--53, January 2004. Google ScholarDigital Library
- Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In ICDM 2008, 2008. Google ScholarDigital Library
- M. Jamali and M. Ester. A matrix factorization technique with trust propagation for recommendation in social networks. In Amatriain et al. {1}. Google ScholarDigital Library
- G. Karypis. Evaluation of item-based top-n recommendation algorithms. In CIKM. ACM, 2001. Google ScholarDigital Library
- Y. Koren, R. M. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. IEEE Computer, 42(8), 2009. Google ScholarDigital Library
- D. Lemire and A. Maclachlan. Slope one predictors for online rating-based collaborative filtering. In Proceedings of SIAM Data Mining (SDM'05), 2005.Google ScholarCross Ref
- G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7, 2003. Google ScholarDigital Library
- P. Marrow, R. Hanbidge, S. Rendle, C. Wartena, and C. Freudenthaler. MyMedia: Producing an extensible framework for recommendation. In NEM Summit, 2009.Google Scholar
- P. Marrow, T. Stevens, I. Kegel, J. Meenowa, and C. McCahill. Future IPTV services field trial report. Technical report, MyMedia project, 2010.Google Scholar
- T.-N. Nguyen, L. Drumond, T. Horvath, A. Nanopoulos, and L. Schmidt-Thieme. Matrix and tensor factorization for predicting student performance. In Proceedings of the 3rd International Conference on Computer Supported Education (CSEDU 2011), 2011.Google Scholar
- M. Piotte and M. Chabbert. The Pragmatic Theory solution to the Netflix Grand Prize, 2009.Google Scholar
- S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. BPR: Bayesian personalized ranking from implicit feedback. In UAI 2009, 2009. Google ScholarDigital Library
- S. Rendle and L. Schmidt-Thieme. Online-updating regularized kernel matrix factorization models for large-scale recommender systems. In RecSys '08: Proceedings of the 2008 ACM Conference on Recommender Systems. ACM, 2008. Google ScholarDigital Library
- M. Weimer, A. Karatzoglou, and A. Smola. Improving maximum margin matrix factorization. Machine Learning, 72(3):263--276, 2008. Google ScholarDigital Library
- C.-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen. Improving recommendation lists through topic diversification. In A. Ellis and T. Hagino, editors, WWW. ACM, 2005. Google ScholarDigital Library
Index Terms
- MyMediaLite: a free recommender system library
Recommendations
Understanding collaborative filtering parameters for personalized recommendations in e-commerce
Collaborative Filtering (CF) is a popular method for personalizing product recommendations for e-Commerce and customer relationship management (CRM). CF utilizes the explicit or implicit product evaluation ratings of customers to develop personalized ...
Peer-Based Recommendations in Online B2C E-Commerce: Comparing Collaborative Personalization and Social Network-Based Personalization
HICSS '12: Proceedings of the 2012 45th Hawaii International Conference on System SciencesWith the widespread diffusion of social network platforms, e-vendors can now use social network information to provide personalized services to their consumers. Nonetheless, the accuracy of social network-based personalization remains uncertain, as ...
How Much Novelty is Relevant?: It Depends on Your Curiosity
SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information RetrievalTraditional recommendation systems (RS's) aim to recommend items that are relevant to the user's interest. Unfortunately, the recommended items will soon become too familiar to the user and hence fail to arouse her interest. Discovery-oriented ...
Comments