Speed up Differential Evolution for ranking of items in recommendation systems

Abstract Recommendation systems can suggest users list of items they have not yet seen but might be interested in. To improve the quality of the generated recommendations, different techniques are often used which try to personalize recommendations. Usually user preferences are stored in the form of a vector in which individual values describe to what extent a given feature is desired by the user. To find this vector, metaheuristic algorithms can be used, however their main drawback is their computational complexity. Therefore, in this paper, a modification of the Differential Evolution algorithm is proposed to enable faster computation of the ranking score for each item in the system, which is used to create a recommendation list. Experiments have been performed on the current MovieLens 25m database and they show that our modification can significantly speed up the process of finding a preference vector, without losing their quality for the top-N recommendation task. We will also address the vulnerability of recommendation systems to profile injection attacks, as a result of which an attacker can influence the generated recommendations.