DESIGNING EXPLANATIONS IN THE RECOMMENDER SYSTEMS BASED ON THE PRINCIPLE OF A BLACK BOX
Serhii Fedorovych Chalyi, Volodymyr Oleksandrovich Leshchynskyi, Irina Oleksandrivna Leshchynska · Advanced Information Systems · 2019
Ab s t r a c t.The subject matter of the article is the process of designing of explanations in the recommender system.The goal is to develop a conceptual model for designing explanations in recommender systems based on the black box principle.Such a model binds the conditions, the result and the constraints on the choice of objects from the user's position.The user should receive justification of the recommendations taking into account context-oriented possibilities of using the proposed objects.Tasks: to adapt the principle of a black box to the task of constructing explanations in the recommender system; to develop a conceptual scheme for constructing explanations according to the functional principle; to develop a conceptual model for the designing` of explanations based on the principle of a black box.The principle used is: functional, or the principle of a black box.The following results are obtained.The principle of the black box to the problem of constructing explanations in the recommender system was adapted.The conceptual scheme of constructing explanations on the basis of a functional principle is developed, taking into account both the properties of objects and the sequences of their use.The conceptual model of the explanation based on the black box principle is developed.Conclusions.Scientific novelty of the results is as follows.The conceptual model for constructing explanations with recommendations on the functional principle or the principle of a black box is proposed.The model takes into account the characteristics of subjects and consumers, information on the use of objects in the subject area, as well as recommendations in the form of a list of objects.The advantage of using the proposed model lies in the fact that it takes into account the methods of applying the recommended objects for constructing explanations.This creates conditions for personalizing recommendations in cases of a cold start of the recommender system, as well as artificial increase in the ratings of individual items.K e ywor d s : recommender systems; e