Advances in Algorithms for Time-Dependent Recommender Systems

Nowadays, Online Social Networks have given the opportunity to users to share their interests. Moreover Location-Based Social Network added the location factor giving a new perspective to users´ check-ins in POIs through smartphones. There are three main parameters characterizing these networks: mobility, proximity and periodicity. Here, we argue that periodicity is a significant upcoming trend in recommender systems. In particular, we present an extended comparison among 9 recommendation frameworks and their structural components. Moreover, we examine whether they provide personalized recommendations or not, the recommendation type they support, the data factors/features they use, the preferred methodology with which they model the problem and the data representation model they have chosen. By gathering this information we give an overview of the techniques and the features used and define new trends in this domain. The main factor is time that refines the final recommendation revealing relations among entities, which can increase accuracy of the proposals.