Autoencoder-based Recommender System Exploiting Natural Noise Removal

Exploiting the Beta Distribution-Based Reputation Model in Recommender System

Managing Natural Noise in Recommender Systems

E-commerce customers demand quick and easy access to suitable products in large purchase spaces. To support and facilitate this purchasing process to users, recommender systems (RSs) help them to find out the information that best fits their preferences and needs in an overloaded search space. These systems require the elicitation of customers’ preferences. However, this elicitation process is ...

Hybrid Recommender System Based on Variance Item Rating

K-nearest neighbors (KNN) based recommender systems (KRS) are among the most successful recent available recommender systems. These methods involve in predicting the rating of an item based on the mean of ratings given to similar items, with the similarity defined by considering the mean rating given to each item as its feature. This paper presents a KRS developed by combining the following app...

Ensemble-based Top-k Recommender System Considering Incomplete Data

Recommender systems have been widely used in e-commerce applications. They are a subclass of information filtering system, used to either predict whether a user will prefer an item (prediction problem) or identify a set of k items that will be user-interest (Top-k recommendation problem). Demanding sufficient ratings to make robust predictions and suggesting qualified recommendations are two si...

Designing a trust-based recommender system in Social Rating Networks

One of the most common styles of business today is electronic business, since it is considered as a principal mean for financial transactions among advanced countries. In view of the fact that due to the evolution of human knowledge and the increase of expectations following that, traditional marketing in electronic business cannot meet current generation’s needs, in order to survive, organizat...