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

Authors: Masoumeh Nafari; Vahid Seydi; Hamidreza Hosseinkhani

Addresses: Department of Computer Science, Science and Research Branch, Islamic Azad University, Tehran, Iran ' Department of Computer, Artificial Intelligence, South Tehran Branch, Islamic Azad University, Tehran, Iran ' Department of Computer Science, Science and Research Branch, Islamic Azad University, Tehran, Iran

Abstract: 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.

Keywords: information retrieval; recommender system; explanations; collaborative filtering; matrix factorisation; gradient descent; topic model; latent Dirichlet allocation; natural language processing; measure of dispersion; coefficient of variation; precision of comment.

DOI: 10.1504/IJSSS.2020.111357

International Journal of Society Systems Science, 2020 Vol.12 No.3, pp.185 - 197

Received: 21 Aug 2019
Accepted: 26 Feb 2020
Published online: 23 Nov 2020 *

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