Context-Aware Recommender System Based on Tensor Factorization

Student: Petrov Aleksei

Educational Programme: Software Engineering (Bachelor)

Final Grade: 9

Year of Graduation: 2017

Collaborative filtering is the most popular recommender system technique. Baseline methods of collaborative filtering do not require knowledge of domain area and have sufficiently high accuracy that allows to use it without tuning in the majority of practical cases. This paper is motivated by the need to build recommender system that will consider additional information noticed context to make more detailed recommendations. To achieve that existing context-aware algorithms were researched and the chosen one, iTALS, was implemented on Apache Spark. Comparison of usage of different context and different algorithm version is direction for future research. This work contains 25 pages, 3 chapters, 3 illustrations, 3 tables, 16 bibliography items, 4 appendicies.

Full text (added May 29, 2017)

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