Information filtering by similarity-preferential diffusion processes
Department of Physics, University of Fribourg - Chemin du Musée 3, CH-1700 Fribourg, Switzerland
Received: 3 October 2013
Accepted: 24 February 2014
Recommender systems provide a promising way to address the information overload problem which is common in online systems. Based on past user preferences, a recommender system can find items that are likely to be relevant to a given user. Two classical physical processes, mass diffusion and heat conduction, have been used to design recommendation algorithms and a hybrid process based on them has been shown to provide accurate and diverse recommendation results. We modify both processes as well as their hybrid by introducing a parameter which can be used to enhance or suppress the weight of users who are most similar to the target user for whom the recommendation is done. Evaluation on two benchmark data sets demonstrates that both recommendation accuracy and diversity are improved for a wide range of parameter values. Threefold validation indicates that the achieved results are robust and the new recommendation methods are thus applicable in practice.
PACS: 89.75.-k – Complex systems / 89.65.-s – Social and economic systems / 89.20.Ff – Computer science and technology
© EPLA, 2014