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Improving Distributed Matrix-Factorization-Based Recommender Systems in MapReduce Framework Using Network Coding

Saeidi, Mohsen | 2019

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 52230 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Jafari Siavoshani, Mahdi
  7. Abstract:
  8. In recent years, highly recommended systems have been used in various areas. One of the approaches of these systems is a collaborative refinement that consists of three user-based, item-based, and matrix-based parsing. Matrix degradation methods are more effective because they allow us to discover the hidden features that exist between user and item interactions and help us better predict recommendations. The low-level mapping method is designed to store and process very high volume of data. In this method, after completing computations in the author’s nodes, the data is sent to the downsizing nodes, which is referred to as ”data spoofing”. It has been observed that in many applications, the operation is very timely. The purpose of this applied project is to reduce the response time of the recommender systems based on matrix decomposition by network coding. To do this, we first intend to implement a matrix-based analyzer-based system in the mapping framework. We then examine how to apply network coding methods to improve the performance of such a system. Finally, with practical implementation, we compare the parameters of these two methods from different aspects
  9. Keywords:
  10. Recommender System ; Map Reduce Processing ; Network Coding ; Distributed Computing ; Machine Learning ; Performance Evaluation

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