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Zhu, L. and Yang, Y. (2015) Refine Item-Based Collaborative Filtering Algorithms with Skew Amplificatio. College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai.

has been cited by the following article:

• TITLE: An Ensemble Learning Recommender System for Interactive Platforms

  AUTHORS: Bernabe Batchakui, Basiliyos Tilahun Betru, Dieudonné Alain Biyong, Lauris Djilo Tchuenkam

  KEYWORDS: Interactive Platforms, Recommender System, Hybrid Recommender, Probabilistic Model, Matrix Factorization

  JOURNAL NAME: World Journal of Engineering and Technology, Vol.10 No.2, May 30, 2022

  ABSTRACT: In interactive platforms, we often want to predict which items could be more relevant for users, either based on their previous interactions with the system or their preferences. Such systems are called Recommender Systems. They are divided into three main groups, including content-based, collaborative and hybrid recommenders. In this paper, we focus on collaborative filtering and the improvement of the accuracy of its techniques. Then, we suggest an Ensemble Learning Recommender System model made of a probabilistic model and an efficient matrix factorization method. The interactions between users and the platform are scored by explicit and implicit scores. At each user session, implicit scores are used to train a probabilistic model to compute the maximum likelihood estimator for the probability that an item will be recommended in the next session. The explicit scores are used to know the impact of the user’s vote on an item at the time of the recommendation.