A Collaborative Filtering Approach Based on Naïve Bayes Classifier

Abstract

Recommender system is an information filtering tool used to alleviate information overload for users on the web. Collaborative filtering recommends items to users based on their historical rating information. There are two approaches: memory-based, which usually provides inaccurate but explainable recommendations; and model-based, whose recommendations are more precise but hard to understand. Here we propose a Bayesian model that not only provides us with recommendations as good as matrix factorization models, but these predictions can also be explained. The model is based on both user-based and item-based collaborative filtering approaches, which recommends items by using similar users' and items' information, respectively. Experiments carried out using four datasets present good results compared to several state-ofthe-art baselines, achieving the best performance using the Normalized Discounted Cumulative Gain (nDCG) quality measure and also improving the prediction's accuracy in some datasets.

A Bayesian Inference Based Hybrid Recommender System

“…The authors' approach is based on a matrix factorization technique to reduce the computational cost of their algorithm. The authors in [35], propose a recommendation approach based on a naive Bayesian classifier to predict the probability with which a user rates an item. However, the authors use a uniform probability distribution that does not consider a priori intuitions related to the item's popularity, the consumption habits adopted by the majority of users.…”

“…where g u (d|D) and g i (b|B) are the Bayesian predictive distributions respectively from the user-based approach and the item-based approach. According to the MAP principle [35], ther ui predicted rating is the rating that maximizes thep ui predictive probability. It is obtained as follows:…”

“…• Naive Bayesian Collaborative Filtering (NBCF) method is a recommendation approach based on the Naive Bayes classifier in which uniform probability distributions are used and therefore simplify the modeling of interactions between users and items [35].…”

Improving k Nearest Neighbors and Naïve Bayes Classifiers Through Space Transformations and Model Selection

“…For instance, recently, Niazi et al [48] use NB to monitor and maintain photovoltaic modules; Shen et al [60] use it to handle dependencies in medical ontologies. In [63], Valdiviezo-Diaz et al use NB for collaborative filtering in Recommender Systems. Despite its simplicity, NB has competitive performances in several domains while maintaining some degree of explainability.…”

Big Data Analysis Based on Machine Learning Techniques

“…This model is based on both product information as well as user features and collective filtering is used in this model. This model is equivalent to matrix factorization models [18].…”