Personalized bundle recommendation using preference elicitation and the Choquet integral - Robbi, Erich; Bronzini, Marco; Viappiani, Paolo; Passerini, Andrea (Frontiers in Artificial Intelligence, 2024)
Bundle recommendation aims to generate bundles of associated products that users tend to consume as a whole under certain circumstances. Modelling the bundle utility for users is a non-trivial task, as it requires to account for the potential interdependencies between bundle attributes. To address this challenge, we introduce a new preference-based approach for bundle recommendation exploiting the Choquet Integral. This allows us to formalize preferences for coalitions of environmental-related attributes, thus recommending product bundles accounting for synergies among product attributes. An experimental evaluation on a dataset of local food products in Northern Italy shows how the Choquet integral allows to naturally formalize a sensible notion of environmental friendliness, and that standard approaches based on weighted sums of attributes end up recommending bundles with lower environmental friendliness even if weights are explicitly learned to maximize it. We further show how preference elicitation strategies can be leveraged to acquire weights of the Choquet Integral from user feedback in terms of preferences over candidate bundles, and show how a handful of queries allow to recommend optimal bundles for a diverse set of user prototypes.