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Recommendation System: a Review of Trust Techniques

EasyChair Preprint no. 11535

5 pagesDate: December 15, 2023


This article highlights the growing application of artificial intelligence (AI) with a particular focus on the use of Implicit Trust-based Recommender Systems (ITRS). These systems leverage trust relationships between users inferred from their past actions such as reviews, check-ins, and clicks. In this article, two types of approaches are discussed. Firstly, explicit trust approaches which aim to improve the accuracy of recommendations by taking into account users' explicit declarations of trust. And secondly, implicit trust approaches are examined, which utilize implicit trust relationships among users inferred from their past behaviors. The overall analysis of these two approaches underscores the benefits of implicit trust in reducing the need for active user participation and alleviating the cold start problem of data sparsity. In conclusion, the article opens the way to new perspectives for item recommendation in the field of smart tourism using AI.

Keyphrases: cold start, Data Sparsity, Explicit Trust, implicit trust, Recommendation Systems

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Sarah Medjroud and Nassim Dennouni and Mourad Loukam},
  title = {Recommendation System: a Review of Trust Techniques},
  howpublished = {EasyChair Preprint no. 11535},

  year = {EasyChair, 2023}}
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