A Coverage-Based Approach to Recommendation Diversity On Similarity Graph

Shameem Ahamed Puthiya Parambath (1, 2) , Nicolas Usunier (1) , Yves Grandvalet (1)

1 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]
2 Labex MS2T - Laboratoire d'Excellence "Maîtrise des Systèmes de Systèmes Technologiques"

We consider the problem of generating diverse, personalized recommendations such that a small set of recommended items covers a broad range of the user's interests. We represent items in a similarity graph, and we formulate the relevance/diversity trade-off as finding a small set of unrated items that best covers a subset of items positively rated by the user. In contrast to previous approaches, our method does not rely on an explicit trade-off between a relevance objective and a diversity objective, as the estimations of relevance and diversity are implicit in the coverage criterion. We show on several benchmark datasets that our approach compares favorably to the state-of-the-art diversification methods according to various relevance and diversity measures.

Domaines: Recherche d'information [cs.IR]; Apprentissage [cs.LG]

Shameem Ahamed Puthiya Parambath, Nicolas Usunier, Yves Grandvalet. A Coverage-Based Approach to Recommendation Diversity On Similarity Graph. 10th ACM Conference on Recommender Systems (RecSys '16), Sep 2016, Boston, United States. pp.15--22, ⟨10.1145/2959100.2959149⟩. ⟨hal-01387171⟩