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.