Visible to the public Abnormal Item Detection Based on Time Window Merging for Recommender Systems

TitleAbnormal Item Detection Based on Time Window Merging for Recommender Systems
Publication TypeConference Paper
Year of Publication2018
AuthorsQi, L. T., Huang, H. P., Wang, P., Wang, R. C.
Conference Name2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)
ISBN Number978-1-5386-4388-4
Keywordsabnormal item detection, abnormal item detection method, ATIAR, average time interval of abnormal rating, Big Data, CFRS, collaborative filtering, collaborative filtering recommendation system, computational cost, Conferences, DAR, data privacy, deviation of time interval of abnormal rating, distribution characteristics, DTIAR, false user profile recognition, Handheld computers, high detection rate, human factors, low false alarm rate, MovieLens, object detection, partition rating time series, pubcrawl, rating distribution characteristics RAR, ratio of abnormal rating, recommender systems, resilience, Resiliency, Scalability, security, security of data, shilling attacks, suspicious intervals, time overhead, time series, time window merging

CFRS (Collaborative Filtering Recommendation System) is one of the most widely used individualized recommendation systems. However, CFRS is susceptible to shilling attacks based on profile injection. The current research on shilling attack mainly focuses on the recognition of false user profiles, but these methods depend on the specific attack models and the computational cost is huge. From the view of item, some abnormal item detection methods are proposed which are independent of attack models and overcome the defects of user profiles model, but its detection rate, false alarm rate and time overhead need to be further improved. In order to solve these problems, it proposes an abnormal item detection method based on time window merging. This method first uses the small window to partition rating time series, and determine whether the window is suspicious in terms of the number of abnormal ratings within it. Then, the suspicious small windows are merged to form suspicious intervals. We use the rating distribution characteristics RAR (Ratio of Abnormal Rating), ATIAR (Average Time Interval of Abnormal Rating), DAR(Deviation of Abnormal Rating) and DTIAR (Deviation of Time Interval of Abnormal Rating) in the suspicious intervals to determine whether the item is subject to attacks. Experiment results on the MovieLens 100K data set show that the method has a high detection rate and a low false alarm rate.

Citation Keyqi_abnormal_2018