Thesis Details

Recommender System for Web Articles

Bachelor's Thesis Student: Kočí Jan Academic Year: 2018/2019 Supervisor: Fajčík Martin, Ing.
Czech title
Doporučovací systém pro webové články
Language
English
Abstract

Recommender systems for web articles are the main interest of this thesis. It explains the most popular approaches used to build these systems, proposes a neural-network-based architecture applying the Skip-gram inspired negative sampling method to the recommendation problem, implements this architecture together with several other models, using Singular value decomposition, collaborative filtering with Alternating Least Squares (ALS) algorithm and a content-based approach using the Doc2Vec algorithm to create document vectors from the obtained articles. Finally, it implements three evaluation metrics - namely the RANK metric, Recall at k and Precision at k - and compares the models with state-of-the-art. Apart from that it also gives a brief discussion on the role and purpose of these systems together with the motivation of using them.

Keywords

Recommender Systems, Machine Learning, Deep Learning, Document Embedding, Collaborative Filtering, Matrix Factorization, Content-based filtering.

Department
Degree Programme
Information Technology
Files
Status
defended, grade B
Date
14 June 2019
Reviewer
Committee
Černocký Jan, prof. Dr. Ing. (DCGM FIT BUT), předseda
Hliněná Dana, doc. RNDr., Ph.D. (DMAT FEEC BUT), člen
Jaroš Jiří, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Orság Filip, Ing., Ph.D. (DITS FIT BUT), člen
Rychlý Marek, RNDr., Ph.D. (DIFS FIT BUT), člen
Citation
KOČÍ, Jan. Recommender System for Web Articles. Brno, 2019. Bachelor's Thesis. Brno University of Technology, Faculty of Information Technology. 2019-06-14. Supervised by Fajčík Martin. Available from: https://www.fit.vut.cz/study/thesis/22020/
BibTeX
@bachelorsthesis{FITBT22020,
    author = "Jan Ko\v{c}\'{i}",
    type = "Bachelor's thesis",
    title = "Recommender System for Web Articles",
    school = "Brno University of Technology, Faculty of Information Technology",
    year = 2019,
    location = "Brno, CZ",
    language = "english",
    url = "https://www.fit.vut.cz/study/thesis/22020/"
}
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