A Random-Walk Based Scoring Algorithm Applied to Recommender Engines

Recommender systems are an emerging technology that helps consumers find interesting products and useful resources. A recommender system makes personalized product suggestions by extracting knowledge from the previous users’ interactions. In this paper, we present ”ItemRank”, a random–walk based scoring algorithm, which can be used to rank products according to expected user preferences, in order to recommend top–rank items to potentially interested users. We tested our algorithm on a standard database, the MovieLens data set, which contains data collected from a popular recommender system on movies and that has been widely exploited as a benchmark for evaluating recently proposed approaches to recommender systems (e.g. [1, 2]). We compared ItemRank with other state-of-the-art ranking techniques on this task. Our experiments show that ItemRank performs better than the other algorithms we compared to and, at the same time, it is less complex with respect to memory usage and computational cost too. The presentation of the method is accompanied by an analysis of the MovieLens data set main properties.