Jean-Francois Pessiot, Tuong-Vinh Truong, Nicolas Usunier, Massih-Reza Amini, Patrick Gallinari


Up to now, most contributions to collaborative filtering rely on rating prediction to generate the recommendations. We, instead, try to correctly rank the items according to the users’ tastes. First, we define a ranking error function which takes available pairwise preferences between items into account. Then we design an effective algorithm that optimizes this error. Finally we illustrate the proposal on a standard collaborative filtering dataset. We adapted the evaluation protocol proposed by (Marlin, 2004) for rating prediction based systems to our case, where pairwise preferences are predicted instead. The preliminary results are between those of two reference rating prediction based methods. We suggest different directions to further explore our ranking based approach for collaborative filtering.


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Paper Citation

in Harvard Style

Pessiot J., Truong T., Usunier N., Amini M. and Gallinari P. (2007). LEARNING TO RANK FOR COLLABORATIVE FILTERING . In Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-972-8865-89-4, pages 145-151. DOI: 10.5220/0002396301450151

in Bibtex Style

author={Jean-Francois Pessiot and Tuong-Vinh Truong and Nicolas Usunier and Massih-Reza Amini and Patrick Gallinari},
booktitle={Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},

in EndNote Style

JO - Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
SN - 978-972-8865-89-4
AU - Pessiot J.
AU - Truong T.
AU - Usunier N.
AU - Amini M.
AU - Gallinari P.
PY - 2007
SP - 145
EP - 151
DO - 10.5220/0002396301450151