Research in recommender systems focuses on applications such as in online shopping malls and simple information systems. These systems consider user profile information and item information obtained from data explicitly entered by users. In these systems, it is possible to classify the items involved and to make recommendations based on a direct mapping from user or user group to item or item g...

We present a user of model interaction based on the physics of kinetic exchange, and extend it to individuals placed in a grid with local interaction. We show with numerical analysis and partial analytical results that the critical symmetry breaking transitions and percolation effects typical of the full interaction model do not take place if the range of interaction is limited, allowing for th...

Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users’ personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy. In this paper, we propose a novel recommender system with the capability of continuously i...

[A Probabilistic Reciprocal Recommender with Temporal Dynamics] Recommender systems are methods of personalisation that provide online services users with suggestions for further interaction with those services. Most recommender systems are for product-to-consumer recommendation, suggesting items or products to users, but there is a growing need for reciprocal recommenders, where the goal is to...

This paper points out that achievements in the field of multimedia analysis and retrieval represent an important opportunity for improvement of recommender system mechanisms. Online shopping systems use various recommender systems; however a study of different approaches has shown that they do not exploit the potential of information carried by multimedia product data for product recommendation...