Fábio Colaço
ABSTRACT
Frameworks that aid the development of Recommender Systems (RSs) are extremely important, since they reduce their development cost by offering reusable tools, as well as implementations of common strategies and popular models. However, it is still hard to find a framework that also provides full abstraction over data set conversion, support for deep learning-based approaches, extensible models and reproducible evaluations. This work introduces a new framework that not only provides several modules to avoid repetitive development work, but also to assist practitioners with these existing challenges. Our evaluation procedure ensures that RSs developed using this new approach are consistent and extensible, by analysing their predictive performance and certain characteristics of their implementation.
- Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, 2016. Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16). 265–283.Google Scholar
- M. Barros, A. Moitinho, and F. M. Couto. 2019. Using Research Literature to Generate Datasets of Implicit Feedback for Recommending Scientific Items. IEEE Access 7(2019), 176668–176680. https://doi.org/10.1109/ACCESS.2019.2958002Google ScholarCross Ref
- Joeran Beel, Stefan Langer, Marcel Genzmehr, Bela Gipp, Corinna Breitinger, and Andreas Nürnberger. 2013. Research paper recommender system evaluation: a quantitative literature survey. In Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation. 15–22.Google ScholarDigital Library
- Maurizio Ferrari Dacrema, Paolo Cremonesi, and Dietmar Jannach. 2019. Are we really making much progress? A worrying analysis of recent neural recommendation approaches. In Proceedings of the 13th ACM Conference on Recommender Systems. 101–109.Google ScholarDigital Library
- Michael D Ekstrand. 2018. The LKPY package for recommender systems experiments: Next-generation tools and lessons learned from the LensKit project. arXiv preprint arXiv:1809.03125(2018).Google Scholar
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web. 173–182.Google ScholarDigital Library
- Nicolas Hug. 2017. Surprise, a Python library for recommender systems. http://surpriselib.com.Google Scholar
- Maciej Kula. 2017. Spotlight. https://github.com/maciejkula/spotlight.Google Scholar
- Wes McKinney 2011. pandas: a foundational Python library for data analysis and statistics. Python for High Performance and Scientific Computing 14, 9 (2011).Google Scholar
- Fernando Ortega, Bo Zhu, Jesús Bobadilla, and Antonio Hernando. 2018. CF4J: Collaborative filtering for Java. Knowledge-Based Systems 152 (2018), 94–99.Google ScholarDigital Library
- Massimo Quadrana, Paolo Cremonesi, and Dietmar Jannach. 2018. Sequence-aware recommender systems. ACM Computing Surveys (CSUR) 51, 4 (2018), 1–36.Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618(2012).Google Scholar
- Lina Yao Bin Wu Aixin Sun Shuai Zhang, Yi Tay. 2019. DeepRec: An Open-source Toolkit for Deep Learning based Recommendation. arXiv preprint arXiv:1905.10536.Google Scholar
- Yao Wu, Christopher DuBois, Alice X Zheng, and Martin Ester. 2016. Collaborative denoising auto-encoders for top-n recommender systems. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. 153–162.Google ScholarDigital Library
- Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen. 2017. Deep Matrix Factorization Models for Recommender Systems.. In IJCAI. 3203–3209.Google Scholar
- Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys (CSUR) 52, 1 (2019), 1–38.Google ScholarDigital Library
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