This page contains support material for the paper: A Gharahighehi and C Vens. “Diversification in Session-based News Recommender Systems”, submitted for the theme issue on Intelligent Systems for Tackling Online Harms of the journal of Personal and Ubiquitous Computing.
This research is built on implementation by Malte Ludewig, Noemi Mauro, Sara Latifi and Dietmar Jannach [1]. In this paper we make rule-based and neighborhood based session-based recommenders, diversity-aware using news article embeddings.
Four datasets are used in this study:
- Adressa [2]: You can download the dataset from this link.
- Globo.com [3]: You can download the dataset from this link.
- Kwestie
- Roularta
The diversification approach can be set in the main.py file. For instance "D" refers to divers neighbor/rule approach.
[1] Ludewig, M., Mauro, N., Latifi, S., Jannach, D. 2019. Performance comparison of neural andnon-neural approaches to session-based recommendation. In: Proceedings of the 13thACM Conference on Recommender Systems, pp. 462–466.
[2] Jon Atle Gulla, Lemei Zhang, Peng Liu, Özlem Özgöbek, and Xiaomeng Su. 2017. The Adressa dataset for news recommendation. InProceedings of theinternational conference on web intelligence. 1042–1048.
[3] P Moreira Gabriel De Souza, Dietmar Jannach, and Adilson Marques Da Cunha. 2019. Contextual hybrid session-based news recommendation withrecurrent neural networks.IEEE Access7 (2019), 169185–169203.