A recommender model based on trust value and time decay: Improve the quality of product rating score in E-commerce platforms

Most of the existing products rating score algorithms do not take fake accounts and time decay of users' ratings into account when creating the list of recommendations. The trust values and the time decay of users' ratings to an item may improve the quality of product rating score in e-commerce platforms, especially when it is thought that nowadays the majority of customers read the reviews before making a purchase. In this paper, we first introduce the concept trust value of users by explaining its mathematical definition and redefine the product rating score based on users' trust relationship. Then we calculate the product rating score based on time decay by making the concept time decay clear. After that we execute both algorithms together in order to show their both effects on the quality of product rating score. Finally, we present experimentally effectiveness of three approaches on a large real dataset.