Разработка подходов к улучшению качества контекстных рекомендательных систем и алгоритмовDevelopment of approaches to improving the quality of contextual recommendation systems and algorithms

This thesis is devoted to context-based recommender systems, particularly to novel methods for enhancing existing algorithms and proposing new approaches for incorporating auxiliary context information, such as the concrete time of user-item interactions and its various derivatives, including time intervals between events. The problems of the next item and the next basket prediction are considered and four new methods are proposed (time-aware and time-dependent TIFU-KNN, Time-Aware Item Weighting (TAIW), time-aware GRU4Rec and TiSASRec). For knowledge-based context-aware recommender systems, a fusion of neural networks and a knowledge graph based approach,  TimeKGATLstm, is proposed. All the approaches underwent comprehensive experimental validation using state-of-the-art benchmarking datasets against competing methods, demonstrating the superiority of the proposed methods (in most cases) in terms of relevant quality metrics for recommender systems, which validates that the proposed methods of context incorporation reliably improve performance.