Feature interactions are essential for achieving high accuracy in recommender systems. Many studies take into account the interaction between every pair of features. However, this is suboptimal because some feature may not be that relevant to recommendation result and taking them introduce noise decrease accuracy. To make best out interactions, we propose a graph neural network approach effecti...

We propose to leverage recommender systems from machine learning build large-scale community distribution models for the mammalian fossil record. Recommender are behind most online life today, shopping news personalisation, dating, or selection of study programmes fastest routes. Many work by predicting user preferences items that occur together in profiles. Technically, this setting closely re...

Advances in telecommunications and information technology have allowed the proliferation of mobile and multifunctional devices and their incorporation more and more into physical objects, making new information and services available. A consequent problem of this new scenario is information overload, i.e. users face vast and distributed information sources, and have difficulty in selecting thos...

Collaborative filtering Recommender systems apply data mining techniques to produce personalized recommender system during the online interaction of active users. These systems use variety of techniques for achieving high success on business, banking, finance and other domains. The fast increase in users and products in recent years produces some of the key issues and challenges for recommender...

Despite the existence of different methods, including data mining techniques, available to be used in recommender systems, such systems still contain numerous limitations. They are in a constant need for personalization in order to make effective suggestions and to provide valuable information of items available. A way to reach such personalization is by means of an alternative data mining tech...