METHOD FOR DETECTING SHILLING ATTACKS BASED ON IMPLICIT FEEDBACK IN RECOMMENDER SYSTEMS
Oksana Chala, Lyudmyla Novikova, Larysa Chernyshova, Angelika Kalnitskaya The problem of identifying shilling attacks, which are aimed at forming false ratings of objects in the recommender system, is considered. The purpose of such attacks is to include in the recommended list of items the goods specified by the attacking user. The recommendations obtained as a […]
METHOD FOR DETECTING SHILLING ATTACKS IN E-COMMERCE SYSTEMS USING WEIGHTED TEMPORAL RULES
Oksana Chala, Lyudmyla Novikova, Larysa Chernyshova The problem of shilling attacks detecting in e-commerce systems is considered. The purpose of such attacks is to artificially change the rating of individual goods or services by users in order to increase their sales. A method for detecting shilling attacks based on a comparison of weighted temporal rules […]
METHOD OF FORMING RECOMMENDATIONS USING TEMPORAL CONSTRAINTS IN A SITUATION OF CYCLIC COLD START OF THE RECOMMENDER SYSTEM
Serhii Chalyi, Volodymyr Leshchynskyi, Irina Leshchynska The problem of the formation of the recommended list of items in the situation of cyclic cold start of the recommendation system is considered. This problem occurs when building recommendations for occasional users. The interests of such consumers change significantly over time. These users are considered “cold” when accessing […]
METHOD OF CONSTRUCTING EXPLANATIONS FOR RECOMMENDER SYSTEMS BASED ON THE TEMPORAL DYNAMICS OF USER PREFERENCES
Serhii Chalyi, Volodymyr Leshchynskyi The problem of constructing explanations for recommendations in situations of cold start and shilling attacks is considered. The first situation is characterized by incomplete information about the user’s preferences, and the second is characterized by a distortion of the ratings of items in the recommendation system. A method for constructing explanations for […]