Detailing explanations in the recommender system based on matching temporal knowledge
| Autor: | Volodymyr Leshchynskyi, Serhii Chalyi, Iryna Leshchynska |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Matching (statistics)
Knowledge representation and reasoning Computer science 020209 energy 0211 other engineering and technologies Energy Engineering and Power Technology 02 engineering and technology explanation of recommendations Recommender system Industrial and Manufacturing Engineering temporal rules Order (exchange) Management of Technology and Innovation 021105 building & construction lcsh:Technology (General) 0202 electrical engineering electronic engineering information engineering lcsh:Industry Electrical and Electronic Engineering recommender system Information retrieval Rule sets Applied Mathematics Mechanical Engineering Computer Science Applications Constraint (information theory) Control and Systems Engineering Dynamics (music) lcsh:T1-995 lcsh:HD2321-4730.9 knowledge matching Period (music) |
| Zdroj: | Eastern-European Journal of Enterprise Technologies, Vol 4, Iss 2 (106), Pp 6-13 (2020) |
| ISSN: | 1729-4061 1729-3774 |
| Popis: | 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 |
| Databáze: | OpenAIRE |
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