Itemrank: A random-walk based scoring algorithm for recommender engines

Gori, Marco; Pucci, Augusto; Roma, V; Siena, I

by Marco Gori, Augusto Pucci, V Roma, I Siena

Abstract:

Recommender systems are an emerging technology that helps consumers to find interesting products. 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, that has been widely exploited as a benchmark for evaluating recently proposed approaches to recommender system (e.g. [Fouss et al., 2005; Sarwar et al., 2002]). We compared ItemRank with other state-of-the-art ranking techniques (in particular the algorithms described in [Fouss et al., 2005]). Our experiments show that ItemRank performs better than the other algorithms we compared to and, at the same time, it is less complex than other proposed algorithms with respect to memory usage and computational cost too.

Reference:

Itemrank: A random-walk based scoring algorithm for recommender engines (Marco Gori, Augusto Pucci, V Roma, I Siena), In Proceedings of the 20th international joint conference on Artifical intelligence, 2007.

Bibtex Entry:

@inproceedings{gori2007itemrank,
abstract = {Recommender systems are an emerging technology that helps consumers to find interesting products. 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, that has been widely exploited as a benchmark for evaluating recently proposed approaches to recommender system (e.g. [Fouss et al., 2005; Sarwar et al., 2002]). We compared ItemRank with other state-of-the-art ranking techniques (in particular the algorithms described in [Fouss et al., 2005]). Our experiments show that ItemRank performs better than the other algorithms we compared to and, at the same time, it is less complex than other proposed algorithms with respect to memory usage and computational cost too.},
author = {Gori, Marco and Pucci, Augusto and Roma, V and Siena, I},
booktitle = {Proceedings of the 20th international joint conference on Artifical intelligence},
keywords = {SML-LIB-BIBLIO,lang:ENG},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
organization = {Morgan Kaufmann Publishers Inc.},
pages = {2766--2771},
title = {{Itemrank: A random-walk based scoring algorithm for recommender engines}},
year = {2007}
}