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Communication Dans Un Congrès Année : 2020

Testing the Impact of Semantics and Structure on Recommendation Accuracy and Diversity

Résumé

The Heterogeneous Information Network (HIN) formalism is very flexible and enables complex recommendations models. We evaluate the effect of different parts of a HIN on the accuracy and the diversity of recommendations , then investigate if these effects are only due to the semantic content encoded in the network. We use recently-proposed diversity measures which are based on the network structure and better suited to the HIN formalism. Finally, we randomly shuffle the edges of some parts of the HIN, to empty the network from its semantic content, while leaving its structure relatively unaffected. We show that the semantic content encoded in the network data has a limited importance for the performance of a recommender system and that structure is crucial.
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Dates et versions

hal-02999863 , version 1 (11-11-2020)

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Pedro Ramaciotti Morales, Lionel Tabourier, Raphaël Fournier-S'Niehotta. Testing the Impact of Semantics and Structure on Recommendation Accuracy and Diversity. ASONAM 2020. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2020, La Hague (virtual), Netherlands. ⟨hal-02999863⟩
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