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Conference papers

Learning to Rank for Collaborative Filtering

Abstract : 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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Submitted on : Wednesday, June 22, 2016 - 3:37:06 PM
Last modification on : Friday, January 8, 2021 - 5:34:12 PM


  • HAL Id : hal-01336029, version 1


Jean-François Pessiot, Tuong Vinh Truong, Nicolas Usunier, Massih-Reza Amini, Patrick Gallinari. Learning to Rank for Collaborative Filtering. International Conference on Enterprise Information Systems (ICEIS), Jun 2007, Madeira, Portugal. pp.145-151. ⟨hal-01336029⟩



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