Recommender System for Web Articles

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Goal of this thesis is the implementation of a recommender system, which brings new, personalized experience into the website. Using machine learning techniques, it should combine user’s website browsing history together with website articles characteristics, and recommend most interesting content to the user. It should provide good balance of the content exploitation vs exploration to the user, which is crucial in the ever changing landscape of the software development techniques and frameworks.

Tasks for the thesis:

  1. Describe the role of recommender systems.
  2. Describe the dataset and compare it with publicly available datasets.
  3. Explore current approaches that are appropriate for building recommender systems.
  4. Choose and describe at least one suitable approach, describe how the selected approach differs from the related work.
  5. Describe your evaluation process for the chosen approach.
  6. Implement the recommender system.
  7. Evaluate the system.
  8. If possible, do an ablation study.
  9. Compare achieved results with state-of-the-art.

Leader: Vlastimil Eliáš

Team: Middleware Engineering Services
Location: Brno
Topic: Recommender System for Web Articles

Student: Jan Kočí

University: Brno University of Technology
Type: Bachelor Thesis
Date of Defence: 14.6.2019
Grade: B
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