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Communication Dans Un Congrès Année : 2018

A Probabilistic Model for Intrusive Recommendation Assessment

Résumé

The overwhelming advances in mobile technologies allow recommender systems to be highly contextualized and able to deliver recommendation without an explicit request. However, it is no longer enough for a recommender system to determine what to recommend according to the users' needs, but it also has to deal with the risk of disturbing the user during recommendation. We believe that mobile technologies along with contextual information may help alleviate this issue. In this paper, we address intrusiveness as a probabilistic approach that makes use of the several embedded applications within the user's device and the user's contextual information in order to figure out intrusive recommendations that are subject to rejection. The experiments that we conducted have shown that the proposed approach yields promising results.
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Dates et versions

hal-03623030 , version 1 (29-03-2022)

Identifiants

  • HAL Id : hal-03623030 , version 1
  • OATAO : 22490

Citer

Imen Akermi, Mohand Boughanem, Rim Faiz. A Probabilistic Model for Intrusive Recommendation Assessment. 12th ACM conference series on Recommender Systems (RECSYS 2018), Oct 2018, Vancouver, Canada. pp.441-445. ⟨hal-03623030⟩
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