Research with LensKit

LensKit is intended to be particularly useful in recommender systems research. See the Experiment Quickstart to get started using LensKit in your own research.

If you publish research that uses LensKit, please cite:

For your convenience, here is the BibTeX:

@INPROCEEDINGS{LensKit,
    title = "Rethinking the Recommender Research Ecosystem: Reproducibility, Openness, and {LensKit}",
    booktitle = "Proceedings of the Fifth {ACM} Conference on Recommender Systems",
    author = "Ekstrand, Michael D and Ludwig, Michael and Konstan, Joseph A and Riedl, John T",
    publisher = "ACM",
    pages = "133--140",
    series = "RecSys '11",
    year =  2011,
    url = "http://doi.acm.org/10.1145/2043932.2043958",
    conference = "RecSys '11",
    doi = "10.1145/2043932.2043958"
}

We would also appreciate it if you could send a PDF and citation for your article to ekstrand@acm.org, so we can know where all LensKit is being used.

Research Using LensKit

LensKit has been used in a number of published papers. In this list, we have attempted to collect published research that has used the LensKit software in some capacity.

2018

• Denis Kotkov, Jari Veijalainen, and Shuaiqiang Wang. 2018. How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm. Computing (December 2018). DOI:10.1007/s00607-018-0687-5

• Ludovik Coba, Panagiotis Symeonidis, and Markus Zanker. 2018. Replicating and Improving Top-N Recommendations in Open Source Packages. In Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics (WIMS ’18), 40:1–40:7. doi:10.1145/3227609.3227671

• Yongjian Yang, Yuanbo Xu, En Wang, Jiayu Han, and Zhiwen Yu. 2018. Improving Existing Collaborative Filtering Recommendations via Serendipity-Based Algorithm. IEEE Trans. Multimedia 20, 7 (July 2018), pp. 1888–1900.

• Denis Kotkov. 2018. Serendipity in Recommender Systems. Thesis, Jyväskylä Studies in Computing vol. 281.

• David Shriver. 2018. Assessing the Quality and Stability of Recommender Systems. M.S. thesis, University of Nebraska - Lincoln.

• Toon De Pessemier and Luc Martens. Heart rate monitoring, activity recognition, and recommendation for e-coaching. Multimedia Tools and Applications. doi:10.1007/s11042-018-5640-2

• Michael D. Ekstrand, Mucun Tian, Ion Madrazo Azpiazu, Jennifer D. Ekstrand, Oghenemaro Anuyah, David McNeill, and Maria Soledad Pera. 2018. All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness. To appear in Proceedings of the Conference on Fairness, Accountability and Transparency.

2017

• Diogo Nicolau. 2017. Neighborhood Construction through Item Popularity in Collaborative Methods. M.S. thesis, University of Lisbon - Instituto Superior Técnico

• Denis Kotkov, Jari Veijalainen, and Shuaiqiang Wang. 2017 A serendipity-oriented greedy algorithm for recommendations. In Proceedings of the 13th International conference on web information systems and technologies, volume 1, pages 32-40. SCITEPRESS, 2017. doi: 10.5220/0006232800320040.

• Maria Soledad Pera and Yui-Kai Ng. 2017. Recommending books to be exchanged online in the absence of wish lists. Journal of the Association for Information Science and Technology

• Michael D. Ekstrand and Maria Soledad Pera. 2017. The Demographics of Cool: Popularity and Demographic Bias in Recommender Evaluation. In RecSys 2017 Poster Proceedings.

• Marius Lørstad Solvang and Steffen Sand. 2017. Video Recommendation Systems: Finding a Suitable Recommendation Approach for an Application Without Sufficient Data . Master thesis, University of Oslo, August 2017.

• C. Sardianos, I. Varlamis, and M. Eirinaki. 2017. Scaling Collaborative Filtering to Large-Scale Bipartite Rating Graphs Using Lenskit and Spark. In 2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService), 70–79. DOI:10.1109/BigDataService.2017.28.

• Harris Papadakis, Costas Panagiotakis, and Paraskevi Fragopoulou. 2017. SCoR: A Synthetic Coordinate based Recommender system. Expert Syst. Appl. 79, (August 2017), 8–19. DOI:10.1016/j.eswa.2017.02.025.

• Rajani Chulyadyo and Philippe Leray. 2017. A Framework for Offline Evaluation of Recommender Systems based on Probabilistic Relational Models. Laboratoire des Sciences du Numérique de Nantes; Capacités SAS. Technical report HAL-01666117.

• Yuchen Zhao. 2017. Recommending Privacy Preferences in Location-Sharing Services. Ph.D thesis, University of St. Andrews, Scotland.

• F.B. Kharrat, A. Elkhleifi, R. Faiz. 2017. Recommendation system based contextual analysis of Facebook comment. In Proceedings of the 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA).

• Michael D. Ekstrand and Vaibhav Mahant. 2017. Sturgeon and the Cool Kids: Problems with Top-N Recommender Evaluation. In Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference. AAAI Press.

• Sushma Channamsetty and Michael D. Ekstrand. 2017. Recommender Response to Diversity and Popularity Bias in User Profiles. In Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference. AAAI Press.

2016

• Shuo Chang. 2016. Leveraging Collective Intelligence in Recommender System. Ph.D dissertation, University of Minnesota, Minneapolis, MN, USA.

• Tien Nguyen. 2016. Enhancing User Experience With Recommender Systems Beyond Prediction Accuracies. Ph.D dissertation, University of Minnesota, Minneapolis, MN, USA.

• Toon de Pessemier, Jeroen Dhondt, Kris Vanhecke, and Luc Martens. 2015. TravelWithFriends: a hybrid group recommender system for travel destinations. In Proceedings of the Workshop on Tourism Recommender Systems (TouRS15), in conjunction with the 9th ACM Conference on Recommender Systems (RecSys 2015).

• Safair Najafi and Ziad Salam. 2016. Evaluating Prediction Accuracy for Collaborative Filtering Algorithms in Recommender Systems. Degree Project in Technology, KTH Royal Institute of Technology School of Computer Science and Communication, Stockholm, Sweden.

• Lucas Colucci, Prachi Doshi, Kun-Lin Lee, Jiajie Liang, Yin Lin, Ishan Vashishtha, Jia Zhang, and Alvin Jude. 2016. Evaluating Item-Item Similarity Algorithms for Movies. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA ‘16).

• Penghua Yu, Lanfen Lin, and Yuangang Yao. 2016. A Novel Framework to Process the Quantity and Quality of User Behavior Data in Recommender Systems. In Proceedings of the 17th International Conference on Web-Age Information Management; published by Springer-Verlag as Lecture Notes in Computer Science vol. 9658 pp. 231–243. DOI=10.1007/978-3-319-39937-9_18.

• Toon de Pessemier, Jeroen Dhondt, and Luc Martens. 2016. Hybrid group recommendations for a travel service. Multimedia Tools and Applications vol. 75 no. 5 (March 2016). Springer. DOI=10.1007/s11042-016-3265-x.

• F. Maxwell Harper and Joseph A. Konstan. The MovieLens Datasets: History and Context. Transactions on Interactive Intelligent Systems vol. 5 no. 4 (January, 2016). ACM. DOI=10.1145/2827872.

2015

• A. Elkhelifi, F. Ben Kharrat, and R. Faiz. 2015. Recommendation Systems Based on Online User’s Action. In Proceedings of the 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic, and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM). IEEE. DOI=10.1109/CIT/IUCC/DASC/PICOM.2015.69.

• F. Maxwell Harper, Funing Xu, Harmanpreet Kaur, Kyle Condiff, Shuo Chang, and Loren Terveen. 2015. Putting Users in Control of Their Recommendations. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys ’15). ACM. DOI=10.1145/2792838.2800179.

• Michael D. Ekstrand, Daniel Kluver, F. Maxwell Harper, and Joseph A. Konstan. 2015. Letting Users Choose Recommender Algorithms: An Experimental Study. In Proceedings of the Ninth ACM Conference on Recommender Systems (RecSys ‘15). ACM. DOI=10.1145/2792838.2800195.

• Axel Magnuson, Vijay Dialani, and Deepa Mallela. 2015. Event Recommendation Using Twitter Activity. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys ’15). ACM. DOI=10.1145/2792838.2796556.

• Martin Wischenbart, Sergio Firmenich, Gustavo Rossi, and Manuel Wimmer. 2015. Recommender Systems for the People — Enhancing Personalization in Web Augmentation. In Proceedings of the RecSys ‘15 Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntIRS ‘15).

• Maria Soledad Pera and Yiu-kai Ng. 2015. A Recommendation-Based Book-Exchange System Without Using Wish Lists. In Poster Proceedings of ACM RecSys 2015.

• Nevena Dragovic and Maria Soledad Pera. 2015. Exploiting Reviews to Guide Users’ Selections. In Poster Proceedings of ACM RecSys 2015.

• Santiago Larraín, Denis Parra, and Alvaro Soto. 2015. Towards Improving Top-N Recommendation by Generalization of SLIM. In Poster Proceedings of ACM RecSys 2015.

• Ioannis T. Christou, Emmanouil Amolochitis, and Zheng-Hua Tan. 2015. AMORE: design and implementation of a commercial-strength parallel hybrid movie recommendation engine. Knowl Inf Syst (August 2015), 1–26. DOI=10.1007/s10115-015-0866-z.

• Nipa Chowdhury , Xiongcai Cai, Cheng Luo. 2015. BoostMF: Boosted Matrix Factorization for Collaborative Ranking. Machine Learning and Knowledge Discovery in Databases, Lecture Notes in Computer Science vol. 9285. Springer. DOI=10.1007/978-3-319-23525-7_1.

• Jeroen Dhondt. 2015. A Hybrid Group Recommender System for Travel Destinations. M.S. thesis, University of Gent.

• Shuo Chang, F. Maxwell Harper, and Loren Terveen. 2015. Using Groups of Items for Preference Elicitation in Recommender Systems. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing. CSCW ’15. New York, NY, USA: ACM, 1258–1269. DOI=10.1145/2675133.2675210.

• Yinzhi Cao and Junfeng Yang. 2015. Towards Making Systems Forget with Machine Unlearning. In Proceedings of the 36th IEEE Symposium on Security and Privacy. IEEE.

• Benjamin Kille et al. 2015. Stream-Based Recommendations: Online and Offline Evaluation as a Service. In Josiane Mothe et al., eds. Experimental IR Meets Multilinguality, Multimodality, and Interaction. Lecture Notes in Computer Science. Springer International Publishing, 497–517. DOI=10.1007/978-3-319-24027-5_48.

• Abhijeet Ghoshal, Syam Menon, and Sumit Sarkar. 2015. Recommendations Using Information from Multiple Association Rules: A Probabilistic Approach. Information Systems Research, vol. 26, iss. 3, pp. 532–551. DOI=10.1287/isre.2015.0583.

• Frederik Ek and Robert Stigsson. 2015. Recommender Systems; Contextual Multi-Armed Bandit Algorithms for the purpose of targeted advertisement within e-commerce. Masters’ thesis. Gothenburg, Sweden: Chalmers University of Technology.

2014

• Alan Said and Alejandro Bellogin. 2014. Comparative Recommender System Evaluation: Benchmarking Recommendation Frameworks. In Proceedings of the Eighth ACM Conference on Recommender Systems (RecSys ‘14). ACM. DOI=10.1145/2645710.2645746.

  This work introduces and uses Rival, a cross-framework recommender evaluation toolkit supporting LensKit, Mahout, and others.

• Daniel Kluver and Joseph A. Konstan. 2014. Evaluating Recommender Behavior For New Users. In Proceedings of the Eighth ACM Conference on Recommender Systems (RecSys ‘14). ACM. DOI 10.1145/2645710.2645742.

• Michael D. Ekstrand, F. Maxwell Harper, Martijn C. Willemsen, and Joseph A. Konstan. 2014. User Perception of Differences in Movie Recommendation Algorithms. In Proceedings of the Eighth ACM Conference on Recommender Systems (RecSys ‘14). ACM. DOI 10.1145/2645710.2645737.

• Tien T. Nguyen. 2014. Improving Recommender Systems: User Roles and Lifecycles. In Proceedings of the 8th ACM Conference on Recommender Systems. RecSys ’14. ACM, 417–420. DOI=10.1145/2645710.2653363.

• Nikos Manouselis and Giannis Stoitsis. 2014. Towards an e-Science Environment for Collaborative Filtering Researchers. International Journal of Digital Library Systems vol 4 no 1. 32pp. IGI Global. DOI=10.4018/ijdls.2014010104.

• Abhijeet Ghoshal and Sumit Sakar. 2014. Association Rules for Recommendations with Multiple Items. INFORMS Journal on Computing 26(3):433–448. DOI 10.1287/ijoc.2013.0575.

• Michael D. Ekstrand. 2014. Towards Recommender Engineering: Tools and Experiments for Identifying Recommender Differences. Ph.D thesis, University of Minnesota.

• E. Amolochitis, I.T. Christou, and Zheng-Hua Tan. 2014. Implementing a Commercial-Strength Parallel Hybrid Movie Recommendation Engine. IEEE Intelligent Systems 29, 2 (March 2014), 92–96. DOI=10.1109/MIS.2014.23

• Yuchen Zhao, Juan Ye, and Tristan Henderson. 2014. Privacy-aware Location Privacy Preference Recommendations. In Proceedings of the 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. MOBIQUITOUS ’14. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 120–129. DOI=10.4108/icst.mobiquitous.2014.258017

• Joseph A. Konstan, J.D. Walker, D. Christopher Brooks, Keith Brown, and Michael D. Ekstrand. 2014. Teaching Recommender Systems at Large Scale: Evaluation and Lessons Learned from a Hybrid MOOC. In Proceedings of the First ACM Conference on Learning @ Scale (ACM L@S ’14). ACM. DOI=10.1145/2556325.2566244.

2013

• Tien T. Nguyen, Daniel Kluver, Ting-Yu Wang, Pik-Mai Hui, Michael D. Ekstrand, Martijn C. Willemsen, and John Riedl. 2013. Rating Support Interfaces to Improve User Experience and Recommender Accuracy. In Proceedings of the Seventh ACM Conference on Recommender Systems (RecSys ‘13). ACM, New York, NY, USA. DOI=10.1145/2507157.2507188.

2012

• Daniel Kluver, Tien T. Nguyen, Michael Ekstrand, Shilad Sen, and John Riedl. 2012. How Many Bits per Rating?. In Proceedings of the Sixth ACM Conference on Recommender Systems (RecSys ‘12). ACM, New York, NY, USA, 99-106. DOI=10.1145/2365952.2365974.

• Michael Ekstrand and John Riedl. 2012. When Recommenders Fail: Predicting Recommender Failure for Algorithm Selection and Combination. Short paper in Proceedings of the Sixth ACM Conference on Recommender Systems (RecSys ‘12). ACM, New York, NY, USA, 233-236. DOI=10.1145/2365952.2366002.

• Guimerà, R., Llorente, A., Moro, E. and Sales-Pardo, M., 2012. Predicting Human Preferences Using the Block Structure of Complex Social Networks. PLoS ONE, 7(9), p.e44620. DOI=10.1371/journal.pone.0044620.

• Sebastian Schelter, Christoph Boden, and Volker Markl. 2012. Scalable Similarity-based Neighborhood Methods with MapReduce. In Proceedings of the Sixth ACM Conference on Recommender Systems. RecSys ’12. New York, NY, USA: ACM, 163–170. DOI=10.1145/2365952.2365984.

2011

• Michael D. Ekstrand, Michael Ludwig, Joseph A. Konstan, and John T. Riedl. 2011. Rethinking The Recommender Research Ecosystem: Reproducibility, Openness, and LensKit. In Proceedings of the Fifth ACM Conference on Recommender Systems (RecSys ’11). ACM, New York, NY, USA, 133-140. DOI=10.1145/2043932.2043958.