With the rapid development of e-commerce,the security issues of recommender systems have been widely investigated.Malicious users can benefit from injecting a great quantities of fake profiles into recommender systems to reduce the frequency of undesired recommendation items.As one of the most important attack methods in recommender systems,the shilling attack has been paid considerable attention,especially to its model and the way to detect it.Among them,the loose version of Group Shilling Attack Generation Algorithm(GSAGenl)has outstanding performance.It can be immune to some PCC(Pearson Correlation Coefficient)-based detectors due to the nature of anti-Pearson correlation.In order to overcome the vulnerabilities caused by GSAGenl,a gravitation-based detection model(GBDM)is presented,integrated with a sophisticated gravitational detector and a decider.And meanwhile two new basic attributes and a particle filter algorithm are proposed for tracking prediction.And then,whether an attack occurs can be determined according to the law of universal gravitation in decision-making.The detection performances of GBDM,HHT-SVM,UnRAP,AP-UnRAP and Semi-SAD are compared and evaluated,when they are simultaneously suffering attacks from GSAGenl.And simulation results show the effectiveness and availability of GBDM.