Designing a Digital Content Recommendation System for films
A.V. Zagranovskaia, D.Yu. Mitiura, T.A. Makarchuk
Available Online 5 May 2020.
- https://doi.org/10.2991/aebmr.k.200502.001How to use a DOI?
- recommendation systems, content filtering, data analysis, vectorization of descriptions method, latent semantic analysis method, proximity metrics, factor analysis
- Purpose of the study. The purpose of the study is to analyze the existing methods for constructing content recommendation systems and developing the most accurate and adequate content recommendation system for films. Materials and methods. The paper considers a variety of data analysis methods: vectorization of descriptions, latent semantic analysis (LSA) and its probabilistic form (pLSA), latent Dirichlet allocation (LDA), proximity metrics (cosine divergence, Jensen-Shannon divergence, Kullback-Leibler divergence), the most common factorization method is truncated SVD. Designed content recommendation systems are tested on MovieLens dataset open data . This opens up opportunities for checking the results obtained and improving the proposed models. Results. As a result, several models of content recommendation systems for films were developed and reviews of potential users of the system were analyzed which allowed determining the best version of the content recommendation system. Conclusion. An analysis of the created recommendation systems made it possible to understand the user requirements for them. The most important criterion was the level of trust to the system. In other words, this is how much the user is sure that he or she will really like offered recommendations. The quality of the recommendations was evaluated using a survey of the system potential users. This made it possible to cover a number of criteria for evaluating recommendation systems, such as accuracy of rating prediction, novelty, surprise and diversity.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - A.V. Zagranovskaia AU - D.Yu. Mitiura AU - T.A. Makarchuk PY - 2020 DA - 2020/05/05 TI - Designing a Digital Content Recommendation System for films BT - 2nd International Scientific and Practical Conference “Modern Management Trends and the Digital Economy: from Regional Development to Global Economic Growth” (MTDE 2020) PB - Atlantis Press SP - 1 EP - 8 SN - 2352-5428 UR - https://doi.org/10.2991/aebmr.k.200502.001 DO - https://doi.org/10.2991/aebmr.k.200502.001 ID - Zagranovskaia2020 ER -