Predicting Item Popularity: Analysing Local Clustering Behaviour of Users

Autor:	Liebig, J., Rao, A.
Rok vydání:	2015
Předmět:	Computer Science - Social and Information Networks
Druh dokumentu:	Working Paper
DOI:	10.1016/j.physa.2015.08.045
Popis:	Predicting the popularity of items in rating networks is an interesting but challenging problem. This is especially so when an item has first appeared and has received very few ratings. In this paper, we propose a novel approach to predicting the future popularity of new items in rating networks, defining a new bipartite clustering coefficient to predict the popularity of movies and stories in the MovieLens and Digg networks respectively. We show that the clustering behaviour of the first user who rates a new item gives insight into the future popularity of that item. Our method predicts, with a success rate of over 65% for the MovieLens network and over 50% for the Digg network, the future popularity of an item. This is a major improvement on current results. Comment: 25 pages, 11 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1503.04404 Zobrazit plný text záznamu View this record from Arxiv