<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Feng Xie</style></author><author><style face="normal" font="default" size="100%">Jiaxing Shang</style></author><author><style face="normal" font="default" size="100%">Zhen Chen</style></author><author><style face="normal" font="default" size="100%">Geoffrey C. Fox</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Grey Forecast Model for Accurate Recommendation in Presence of Data Sparsity and Correlation</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Collaborative Filtering</style></keyword><keyword><style  face="normal" font="default" size="100%">Correlation</style></keyword><keyword><style  face="normal" font="default" size="100%">Grey Forecast Model</style></keyword><keyword><style  face="normal" font="default" size="100%">Recommender Systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Sparsity</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2012</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://grids.ucs.indiana.edu/ptliupages/publications/rsweb12_submission_2.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Indiana University</style></publisher><pub-location><style face="normal" font="default" size="100%">Bloomington</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Recommender systems attract growing attention recently, as they can suggest appropriate choices to users based on intelligent prediction. As one of the most popular recommender system techniques, Collaborative Filtering achieves efficiency from the similarity measurement of users and items. However, existing similarity measurement methods have reduced accuracy due to data correlation and sparsity. To overcome these problems, this paper introduces the Grey Forecast model for recommender systems. Firstly, the Cosine Distance method is used to compute the similarities between items. Then we rank the items, which have been rated by the active user according to their similarities to the target item, which has not yet been rated by the active user and select the first k items’ ratings as input to construct a Grey Forecast model and yield prediction. The novelty of the paper is two-fold: less data is required in constructing the model, and the model will become more effective when strong correlations exist among the data. Our approach was evaluated on two public datasets: MovieLens and EachMovie. The experimental results show that the proposed algorithm can significantly overcome the limitation of the data sparsity and cope with data correlation. In particular, the accuracy of the MovieLens dataset has been improved by over 20% in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), even with small k.</style></abstract></record></records></xml>