PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning to Rank for Collaborative Filtering
Jean-François Pessiot, Vinh Truong, Nicolas Usunier, Massih Amini and Patrick Gallinari
In: ICEIS 2007(2007).


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.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:3696
Deposited By:Jean-François Pessiot
Deposited On:14 February 2008