| dc.description.abstract | With the rapid growth of e-commerce, Recommender system is widely used in various walks of life. Recommender systems can help users make decisions in a complex environment, which is mainly based on the understanding of user and item by Recommender system, and then recommend the item to the user. Thereby helping consumers quickly to find the information or products they may be interested in. The most commonly recommended method is Feature-based Filtering. However, due to the recommender system is in an open type of environment, malicious attacker is easy to inject false information into the rating data, which means manufacture false ratings, opinions or comments to push up or suppress certain items.
In recent years, many detection algorithms have been proposed, however the application of these methods are still subject to different restrictions. For instance, the related papers of the PCA-based method have good results, but its drawback is that the PCA-based cannot handle missing values, leading to a great error. In addition, the use of statistical methods in doing SPC (Statistical Process Control) data clustering, you need a long-term cumulative data for each item in order to make a meaningful grouping and thus the training data.
In this study, we made use of the characteristics of the Beta distribution method to design attack detection. This method can solve the problem of missing values, besides, data clustering does not need to accumulate large amounts of data for some time and it can detect the products which is attacked. In addition, there are other in the past scholars are assuming that the attacker only attacks one target item in the user data, this study simulated lots of attackers attack multiple targets in the experiment. Beta distribution method for the Detection rate is over 70%, the False alarm rate is between 10% to 20%. | en_US |