Recommender Systems - An Introduction
- D. Jannach, M. Zanker, A. Felfernig, G. Friedrich
- Computer Science
- 30 September 2010
An overview of approaches to developing state-of-the-art recommender systems, including current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches.
Beyond accuracy: evaluating recommender systems by coverage and serendipity
- Mouzhi Ge, Carla Delgado-Battenfeld, D. Jannach
- Computer ScienceACM Conference on Recommender Systems
- 26 September 2010
It is argued that the new ways of measuring coverage and serendipity reflect the quality impression perceived by the user in a better way than previous metrics thus leading to enhanced user satisfaction.
When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation
- D. Jannach, Malte Ludewig
- Computer ScienceACM Conference on Recommender Systems
- 27 August 2017
This work shows based on a comprehensive empirical evaluation that a heuristics-based nearest neighbor (kNN) scheme for sessions outperforms GRU4REC in the large majority of the tested configurations and datasets and ensures the scalability of the kNN method.
Evaluation of session-based recommendation algorithms
- Malte Ludewig, D. Jannach
- Computer ScienceUser modeling and user-adapted interaction
- 26 March 2018
An in-depth performance comparison of a number of session-based recommendation algorithms based on recurrent neural networks, factorized Markov model approaches, as well as simpler methods based, e.g., on nearest neighbor schemes reveals that algorithms of this latter class often perform equally well or significantly better than today’s more complex approaches based on deep neural networks.
Are we really making much progress? A worrying analysis of recent neural recommendation approaches
- Maurizio Ferrari Dacrema, P. Cremonesi, D. Jannach
- Computer ScienceACM Conference on Recommender Systems
- 16 July 2019
A systematic analysis of algorithmic proposals for top-n recommendation tasks that were presented at top-level research conferences in the last years sheds light on a number of potential problems in today's machine learning scholarship and calls for improved scientific practices in this area.
Automated Generation of Music Playlists: Survey and Experiments
- G. Bonnin, D. Jannach
- Computer ScienceACM Computing Surveys
- 12 November 2014
The results show that track and artist popularity can play a dominant role and that additional measures are required to better characterize and compare the quality of automatically generated playlists.
Sequence-Aware Recommender Systems
- Massimo Quadrana, P. Cremonesi, D. Jannach
- Computer ScienceACM Computing Surveys
- 23 February 2018
A categorization of the corresponding recommendation tasks and goals is proposed, existing algorithmic solutions are summarized, methodological approaches when benchmarking what the authors call sequence-aware recommender systems are discussed, and open challenges in the area are outlined.
How should I explain? A comparison of different explanation types for recommender systems
- Fatih Gedikli, D. Jannach, Mouzhi Ge
- Computer ScienceInt. J. Hum. Comput. Stud.
- 1 April 2014
A systematic review and taxonomy of explanations in decision support and recommender systems
- Ingrid Nunes, D. Jannach
- Computer ScienceUser modeling and user-adapted interaction
- 5 October 2017
This work systematically review the literature on explanations in advice-giving systems, which includes recommender systems, and derives a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems.
Accuracy improvements for multi-criteria recommender systems
- D. Jannach, Z. Karakaya, Fatih Gedikli
- Computer ScienceACM Conference on Economics and Computation
- 4 June 2012
This work proposes to use Support Vector regression to determine the relative importance of the individual criteria ratings and proposes to combine user- and item-based regression models in a weighted approach, which outperforms both recent single-rating algorithms based on matrix factorization and previous methods based on multi-criteria ratings in terms of the predictive accuracy.
...
...