01835nas a2200289   4500000000100000000000100001008004100002260001200043653002700055653001800082653001700100653002800117100002200145700002800167700001800195700002000213700002000233700002500253700002000278700002000298245008500318856008800403300001000491490000600501520102400507022001401531       2015                            d  c03/201510aRecommendation Systems10aMobile Device10aGraph Theory10aCollaborative Filtering1 aHéctor Cordobés1 aAntonio Fernández Anta1 aLuis Chiroque1 aFernando Pérez1 aAgustín Santos1 aRafael García Leiva1 aPhilippe Morere1 aLorenzo Ornella00aEmpirical Comparison of Graph-based Recommendation Engines for an Apps Ecosystem  uhttp://www.ijimai.org/JOURNAL/sites/default/files/files/2015/02/ijimai20153_2_4.pdf  a33-390 v33 aRecommendation engines (RE) are becoming highly popular, e.g., in the area of e-commerce. A RE offers new items (products or content) to users based on their profile and historical data. The most popular algorithms used in RE are based on collaborative filtering. This technique makes recommendations based on the past behavior of other users and the similarity between users and items. In this paper we have evaluated the performance of several RE based on the properties of the networks formed by users and items. The RE use in a novel way graph theoretic concepts like edges weights or network flow. The evaluation has been conducted in a real environment (ecosystem) for recommending apps to smartphone users. The analysis of the results allows concluding that the effectiveness of a RE can be improved if the age of the data, and if a global view of the data is considered. It also shows that graph-based RE are effective, but more experiments are required for a more accurate characterization of their properties.  a1989-1660