Using principles of locality and connectivity of the context in recommender systems
| Autor: | Volodymyr Oleksandrovich Leshchynskyi, Irina Oleksandrivna Leshchynska |
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| Jazyk: | English<br />Russian<br />Ukrainian |
| Rok vydání: | 2018 |
| Předmět: | |
| Zdroj: | Вісник Національного технічного університету "ХПÌ": Системний аналіз, управління та інформаційні технології, Vol 0, Iss 22, Pp 16-21 (2018) |
| Druh dokumentu: | article |
| ISSN: | 2079-0023 2410-2857 |
| DOI: | 10.20998/2079-0023.2018.22.03 |
| Popis: | The problem of the relevance of input data in advisory systems is investigated. This problem arises due to insufficient differentiation of data on goods relative to consumers, which does not allow to fully individualize their preferences in the advisory system. To solve this problem, it is suggested to take into account the local contexts of consumers, reflecting the conditions for the acceptance of the choice by these consumers. Using the context allows you to set contextual constraints on possible variants of an ordered list of recommendations and thereby improve the quality of the recommendation system. In order to provide context-oriented recommendations, it is proposed to consistently generalize and filter out the local contexts of consumers using the principles of locality and connectivity. The peculiarity of using these principles is that the static and dynamic aspects of the context are combined. The first aspect is characterized by a set of properties of objects that are of interest to the consumer. The second aspect is given in the form of patterns of events reflecting the consumer’s behavior with respect to these objects. The proposed relationship between the aspects is that each event corresponds to a pair of successive sets of object properties that differ in one property value. A two-phase approach to the formation of a decision-making context for a recommendation system is proposed, which provides for the consistent integration of the static and dynamic components of the context. Integration uses an equivalence, similarity and compatibility relationship. When the first phase is implemented, item-based is formed, and the second is a user-based context description. Then these descriptions are combined and filtered in accordance with the characteristics of the new consumer to whom the recommendations are issued. The practical significance of the proposed approach is that it allows you to delete irrelevant input data taking into account the context of the decision-making by the consumer and, on this basis, improve the accuracy of the recommendations. |
| Databáze: | Directory of Open Access Journals |
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