Preferential attachment, aging and weights in recomendation systems

In the current work, pre fe re ntial- attachme nt algorithms are applied to Recommendation Systems in order to improve their quality of prediction from a sparse dataset. We show how some networks are grown under the influence of trendiness forces, and how this can be used to enhance the results of a recommendation system, i. e. increase their percentage of right predictions. After defining a base algorithm, we create recommendation networks which are based on an histogram of user ratings. We show the influence of data aging in the prediction of user habits and how the exact moment of the prediction influences the recommendation. Finally, we design weighted networks that take into account the age of the information used to generate the links. In this way, we obtain a better approximation to evaluate the users’ tastes.