Bayesian Belief Networks – Based Product Prediction for E-Commerce Recommendation

Abstract

Abstract. Prediction systems apply knowledge discovery techniques to the problem of making personalized product recommendations. New recommender system technologies are needed that can quickly produce quality recommendations, even for very large-scale problems. This paper presents a new and efficient approach that works using Bayesian belief networks (BBN) and that calculate the probabilities of inter-dependent events by giving each parent event a weighting (Expert systems). To get best result for the sales prediction, different weights have been applied on the proposed algorithm. Finally we got results for prediction, for a given product, using our proposed algorithm and final recommendation [1] has been made to the customers based on preference similarity.

Online product prediction and recommendation using probability graphical model and collaborative filtering: A new approach

“…A Bayesian Belief Network [3] is a probabilities graphical model that represents a set of random variables and their conditional dependencies -via directed acyclic graph (DAG). Each node is associated with a probability function that takes as input a particular set of values for the node's parent variables and gives the probability of the variable represented by the node.…”