Using precision of users' reviews to improve the performance of matrix factorisation method in recommender systems

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

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Dangosydd eitem ddigidol (DOI)

  • Masoumeh Nafari
    Azad University
  • Vahid Seydi
    Azad University
  • Hamidreza Hosseinkhani
    Azad University
Recommender systems try to discover some latent features of users and items by looking at the available information such as users' history of ratings to items and then use these latent factors to estimate users' interest level to a particular item. Traditional methods such as standard matrix factorisation rely on the ratings that users have submitted explicitly, no matter the impact of each latent factor on the total rating. Textual reviews that are posted by users can provide us some insight into the major motive behind the ratings that can also be used to explain the reasoning behind our suggestion. A first-order gradient method for matrix factorisation is proposed in this paper that considers two parameters while finding optimal latent vectors: 1) the impact of each latent factor on total rating; 2) the precision of each review. Evaluating the method on the YELP dataset shows that the algorithm converges the squared error and improves the performance remarkably.
Iaith wreiddiolSaesneg
Tudalennau (o-i)185-197
Nifer y tudalennau13
CyfnodolynInternational Journal of Society Systems Science
Rhif y cyfnodolyn3
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 6 Tach 2020
Cyhoeddwyd yn allanolIe
Gweld graff cysylltiadau