METHOD OF CONSTRUCTING EXPLANATIONS FOR RECOMMENDER SYSTEMS BASED ON THE TEMPORAL DYNAMICS OF USER PREFERENCES
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 the recommended list of subjects is proposed. The method uses weighted temporal dependencies to form explanations. Each such dependence reflects a change in sales of goods for two non-contiguous time intervals. These intervals are set according to a given level of detail of time, for example, day, week, month. The input is presented by a sales journal with time stamps. The method includes the steps of forming temporal rules, calculating the weights of the rules, building explanations. The weights of the rules reflect the degree of change in sales for a pair of intervals. The result of the method is a recommendation in the form of a numerical estimate of the change in user preferences with respect to the subject in the recommendation. The proposed method allows to increase sales efficiency due to the active selection of items by the user based on the explanations received
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