Detailing explanations in the recommender system based on matching temporal knowledge

Authors

DOI:

https://doi.org/10.15587/1729-4061.2020.210013

Keywords:

recommender system, explanation of recommendations, temporal rules, knowledge matching

Abstract

The problem of matching knowledge in the temporal aspect when constructing explanations for recommendations is considered. Matching allows reducing the influence of conflicting knowledge on the explanation in a recommender system.

A model of knowledge representation in the form of a temporal rule with the explanation constraint is proposed. The temporal rule sets the order for two sets of events of the same type that occurred at two different time intervals in time. An explanation constraint establishes a correspondence between the temporal order represented by the rule for a pair of intervals and the description of temporal dynamics for a given time period. This dynamic is represented by the explanation of the recommendation. The model is designed to match knowledge, taking into account the explanation constraint, as well as further use the matched knowledge to clarify explanations based on the results of the intelligent system.

A method for clarifying explanations in a recommender system based on knowledge matching in the form of temporal rules is developed. The method uses records of purchases of goods, services or their ratings as input data. The method identifies a subset of rules matched in the temporal aspect, which represent the same dynamics of consumer demand for the target item (increase or decrease) as explanations in the recommender system. Matching of temporal knowledge makes it possible to form a refined list of explanations. This list includes basic and clarifying explanations. The basic explanation reflects the dynamics of user interests for the entire given period of time. Clarifying explanation specifies changes in demand for individual intervals within a given time period. The use of the temporal dynamics of user preferences in the explanation is aimed at increasing confidence in the received recommendations

Author Biographies

Serhii Chalyi, Kharkiv National University of Radio Electronics Nauky ave., 14, Kharkiv, Ukraine, 61166

Doctor of Technical Sciences, Professor

Department of Information Control System

Volodymyr Leshchynskyi, Kharkiv National University of Radio Electronics Nauky ave., 14, Kharkiv, Ukraine, 61166

PhD, Associate Professor

Department of Software Engineering

Iryna Leshchynska, Kharkiv National University of Radio Electronics Nauky ave., 14, Kharkiv, Ukraine, 61166

PhD, Associate Professor

Department of Software Engineering

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Published

2020-08-31

How to Cite

Chalyi, S., Leshchynskyi, V., & Leshchynska, I. (2020). Detailing explanations in the recommender system based on matching temporal knowledge. Eastern-European Journal of Enterprise Technologies, 4(2 (106), 6–13. https://doi.org/10.15587/1729-4061.2020.210013

Issue

Vol. 4 No. 2 (106) (2020): Information technology. Industry control systems

Section

Information technology

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Copyright (c) 2020 Serhii Chalyi, Volodymyr Leshchynskyi, Iryna Leshchynska

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