No abstract

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 the recommendation system. A method for building recommendations in a cyclical cold start situation using temporal constraints is proposed. Temporal constraints are formed on the basis of the selection of repetitive pairs of actions for choosing the same objects at a given level of time granulation. Input data is represented by a set of user choice records. For each entry, a time stamp is indicated. The method includes the phases of the formation of temporal constraints, the addition of source data using these constraints, as well as the formation of recommendations using the collaborative filtering algorithm. The proposed method makes it possible, with the help of temporal constraints, to improve the accuracy of recommendations for "cold" users with periodic changes in their interests.

The problem of the online construction of a rating list of objects in the recommender system is considered. A method for constructing recommendations online using the presentation of input data in the form of a multi-layer graph based on changes in user interests over time is proposed. The method is used for constructing recommendations in a situation with implicit feedback from the user. Input data are represented by a sequence of user choice records with a time stamp for each choice. The method includes the phases of pre-filtering of data and building recommendations by collaborative filtering of selected data. At pre-filtering of the input data, the subset of data is split into a sequence of fixed-length non-overlapping time intervals. Users with similar interests and records with objects of interest to these users are selected on a finite continuous subset of time intervals. In the second phase, the pre-filtered subset of data is used, which allows reducing the computational costs of generating recommendations. The method allows increasing the efficiency of building a rating list offered to the target user by taking into account changes in the interests of the user over time.

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