Computer Science Department

Lehrstuhl für Dienstleistungsinformatik / e-Services Research Group

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Dr. Lukas Lerche

Foto von Lukas Lerche Email: Lukas.Lerche [ at ] tu-dortmund.de

Publications

  1. Lerche L.: Using Implicit Feedback for Recommender Systems: Characteristics, Applications, and Challenges, Dissertation, e-Services Research Group - Lehrstuhl Informatik XIII, TU Dortmund, 2016. (DOI)
    Keywords: Recommender Systems, Implicit Feedback, Short-term Recommendations, Reminding, Recommedation Biases, E-Commerce
  2. Jannach, D. and Lerche, L.: Offline Performance vs. Subjective Quality Experience: A Case Study in Video Game Recommendation, ACM Symposium on Applied Computing, ACM SAC 2017, Marrakesh, Morocco
    Keywords: Recommender Systems, Offline-Online Study
  3. Jannach, D., Kamehkhosh, I, and Lerche, L.: Leveraging Multi-Dimensional User Models for Personalized Next-Track Music Recommendation, ACM Symposium on Applied Computing, ACM SAC 2017, Marrakesh, Morocco
    Keywords: Recommender Systems, Music Recommndation
  4. Jannach, D., Lerche, L. amd Zanker, M.: Recommending Based on Implicit Feedback, Chapter 14 in Brusilovsky, P. and He, D.: Social Information Access, Springer, 2017 (forthcoming).
    Keywords: Recommender Systems, Implicit Feedback
  5. Jannach, D., Lerche, L., Bonnin, G.: Empfehlungssysteme, automatische Erzeugung von Wiedergabelisten und Musikdatenbanken, In Rötter, G.: Handbuch der Funktionalen Musik, Springer, 2016 (in print). Springer series
    Keywords: Music, Recommender Systems
  6. Kamehkhosh, I., Jannach, D., Lerche, L.: Personalized Next-Track Music Recommendation with Multi-dimensional Long-Term Preference Signals, Workshop on Multi-dimensional Information Fusion for User Modeling and Personalization (IFUP) at UMAP 2016, Halifax, CA, 2016 (pdf)
    Keywords: Music Recommendation, Long-term Preferences
  7. Lerche, L., Jannach, D., Ludewig, M.: On the Value of Reminders within E-Commerce Recommendations, User Modeling, Adaptation and Personalization (UMAP 2016), Halifax, CA, 2016 (Best Student Paper Award) (pdf)
    Keywords: Recommender Systems, E-Commerce, Reminding
  8. Jannach, D., Jugovac, M., Lerche, L.: Supporting the Design of Machine Learning Workflows with a Recommendation System, ACM Transactions on Intelligent Interactive Systems (ACM TiiS), 6(1), 2016 (pdf)
    Keywords: Recommender Systems, Machine Learning Workflow, RapidMiner
  9. Jannach, D., Lerche, L., Kamehkhosh, I., Jugovac, M.: What recommenders recommend - An analysis of recommendation biases and possible countermeasures, User Modeling and User-Adapted Interaction, 25(5), 2015, pp. 427-491. (2015 James Chen Award for best UMUAI paper) (pdf)
    Keywords: Recommender Systems, Bias, Evaluation Methodology
  10. Jannach, D., Lerche, L., Jugovac, M.: Item familiarity as a possible confounding factor in user-centric recommender systems evaluation, i-Com Journal For Interactive Media, 14(1) 2015, 29-40 (pdf)
    Keywords: Recommender Systems, User Study, Evaluation Methodology
  11. Jannach, D., Lerche, L., Jugovac, M.: Item Familiarity Effects in User-Centric Evaluations of Recommender Systems, Demos and Posters Track, ACM Conference on Recommender Systems (RecSys 2015 Posters), Vienna, 2015. (pdf) (CEUR-WS Proceedings)
    Keywords: Recommender Systems, Evaluation, User Study
  12. Jannach, D., Lerche, L., Jugovac, M.: Adaptation and Evaluation of Recommendations for Short-term Shopping Goals, Proceedings 9th ACM Conference on Recommender Systems (RecSys 2015), Vienna, 2015, pp. 211-218. (pdf)
    Keywords: Recommender Systems, Evaluation, Real-world Data, Contextualization
  13. Jannach, D., Lerche, L. Kamehkhosh, I.: Beyond "Hitting the Hits" - Generating Coherent Music Playlist Continuations with the Right Tracks, Proceedings 9th ACM Conference on Recommender Systems (RecSys 2015), Vienna, 2015, pp. 187-194. (pdf)
    Keywords: Recommender Systems, Music Playlist Generation, Evaluation
  14. Jannach, D., Jugovac, M., Lerche, L.: Adaptive recommendation-based modeling support for data analysis workflows, ACM Conference on Intelligent User Interfaces (IUI 2015), Atlanta, GA, pp. 252-262 (pdf)
    Keywords: Recommendation, Process Modeling, RapidMiner
  15. Lerche, L., Jannach, D.: Using Graded Implicit Feedback for Bayesian Personalized Ranking, ACM Recommender Systems (RecSys 2014), Foster City, CA, pp. 353-357. (pdf)
    Keywords: Recommender Systems, implicit feedback, ranking
  16. Jannach, D., Lerche, L., Gdaniec, M.: Re-ranking recommendations based on predicted short-term interests - A protocol and first experiment, Proceedings of the workshop Intelligent Techniques for Web Personalization and Recommender Systems at AAAI 2013, Bellevue, Washington, 2013. (pdf)
    Keywords: Recommender systems, context, e-commerce
  17. Jannach, D., Lerche, L., Gedikli, G., Bonnin, G.: What recommenders recommend - An analysis of accuracy, popularity, and sales diversity effects, 21st International Conference on User Modeling, Adaptation and Personalization (UMAP 2013), Rome, Italy, pp. 25-37. (pdf)
    Keywords: Recommender Systems, Evaluation
  18. Jannach, D., Lerche L.: Perspektiven in der Offline-Evaluation von Empfehlungsalgorithmen, HMD - Praxis der Wirtschaftsinformatik, 50 (2013) 293, 34-44 (pdf).
    Keywords: Recommender Systems, Evaluation
  19. Lerche L.: Entwurf und Umsetzung von hybriden Empfehlungssystemen, Masters Thesis, e-Services Research Group - Lehrstuhl Informatik XIII, TU Dortmund, 2012.
    Keywords: Recommender Systems, Evaluation