MULTILEVEL PERSONALIZATION OF EXPLANATIONS IN RECOMMENDER SYSTEMS
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Abstract
The subject matter of the article is the process of building a personalized list of objects in recommendation systems. The goal is to develop a generalized formal description of the multi-level presentation of explanations in recommendation systems to personalize these explanations, taking into account the features of the use of recommended subjects. Such a description provides a formal framework for constructing a multi-level model of explanation, taking into account the static and dynamic characteristics of the subject area. Tasks: structuring the multi-level presentation of explanations in recommendation systems taking into account differences in the possibilities of personalizing explanations using data and knowledge; development of a formal presentation of explanations at the levels of data, information, knowledge and metacognition, taking into account the relationships between these levels. The approaches used are: approaches to the construction of explanations based on the similarity of user interests and properties of user demand items. The following results are obtained. The levels of explanation description are structured taking into account knowledge about the context of consumer choice. A formal description of the multi-level presentation of explanations in recommendation systems is proposed. Conclusions. The scientific novelty of the results is as follows. A formal description of the explanations of the recommended personal list of objects in the form of a hierarchy of levels of data, information, knowledge and meta-knowledge about user behavior and characteristics of objects is proposed. At the data level, a description of the variables and their values is given, taking into account the instant of occurrence of these values. Information at the next level is represented by the relationships between individual facts. Knowledge is represented by causal or temporal explanatory rules that generalize the relationship of the information level to a subset of facts. Meta-knowledge sets the key patterns that determine the benefits and relevance of the proposed choice for the user of the recommendation system. In a practical aspect, the proposed formalization of explanations determines the typical sequence of constructing and personalizing multilevel explanations regarding recommendations, taking into account the characteristics of the subject area.
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References
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