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Julian Knoll
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Books and Theses
- 2017
- [b1]Julian Knoll:
Higher-order factorization machines: implementation, application, and comparison of a state-of-the-art recommender approach. University of Erlangen-Nuremberg, Germany, 2017, pp. 1-208
Journal Articles
- 2020
- [j1]Julian Knoll, Johannes Stübinger:
Machine-Learning-Based Statistical Arbitrage Football Betting. Künstliche Intell. 34(1): 69-80 (2020)
Conference and Workshop Papers
- 2021
- [c7]Niklas Scheidthauer, Julian Knoll, Rainer Groß:
Visualizing Customer Journeys: How to Illustrate the Entire Customer Interaction Universe of a Commercial Website in Real Time. I4CS 2021: 167-182 - 2018
- [c6]Tobias Kissmer, Julian Knoll, Stefan Stieglitz, Rainer Groß:
Knowledge Workers' Expectations Towards a Digital Workplace. AMCIS 2018 - [c5]Julian Knoll, Rainer Groß, Axel Schwanke, Bernhard Rinn, Martin Schreyer:
Applying Recommender Approaches to the Real Estate e-Commerce Market. I4CS 2018: 111-126 - [c4]Johannes Stübinger, Julian Knoll:
Beat the Bookmaker - Winning Football Bets with Machine Learning (Best Application Paper). SGAI Conf. 2018: 219-233 - 2017
- [c3]Julian Knoll, David Köckritz, Rainer Groß:
Markov Random Walk vs. Higher-Order Factorization Machines: A Comparison of State-of-the-Art Recommender Algorithms. I4CS 2017: 87-103 - 2016
- [c2]Julian Knoll:
Recommending with Higher-Order Factorization Machines. SGAI Conf. 2016: 103-116 - 2015
- [c1]Michael Grottke, Julian Knoll, Rainer Groß:
How the distribution of the number of items rated per user influences the quality of recommendations. I4CS 2015: 1-8
Coauthor Index
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