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JOURNALS // Artificial Intelligence and Decision Making // Archive

Artificial Intelligence and Decision Making, 2019 Issue 3, Pages 24–31 (Mi iipr177)

Decision analysis

Multi-criteria context-driven recommender systems: model and method

A. V. Smirnov, A. V. Ponomarev

St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS), St. Petersburg, Russia

Abstract: A model and method of generating context-driven recommendations for recommendation systems with multi-criteria ratings are proposed, applicable when the user's attitude to the object is fixed not by using one integral criterion (assessment, overall rating), but by using a set of individual criteria that evaluate different aspects of the object. The proposed model and method allow to solve two main problems of using recommender systems: to rank objects according to the predicted subjective integral utility with given weights of partial criteria and to rank objects according to the predicted subjective integral utility in a given context.

Keywords: recommendation systems, recommender systems, multi-criteria optimization, weighted sum method, collaborative filtering, content filtering, context-driven systems.


 English version:
, 2020, 47:5, 298–303

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© Steklov Math. Inst. of RAS, 2024