Original title: Pravděpodobnostní modely pro doporučovací systémy
Translated title: Probabilistic Models for Recommender Systems
Authors: Ahmadli, Aydin ; Vomlelová, Marta (advisor) ; Peška, Ladislav (referee)
Document type: Master’s theses
Year: 2022
Language: eng
Abstract: Recommender systems are software tools and techniques providing recommendations to users based on their needs. Today, popular e-commerce sites widely use recommender systems to recommend product items, articles, books, music, etc. In this thesis, we discuss various probabilistic models for recommender systems, and put the most focus on implementation of hybrid and interpretable probabilistic content-based collaborative filtering model, called Collaborative Topic model for Poisson distributed ratings (CTMP) augmented with Bernoulli randomness for Online Maximum a Posteriori Estimation (BOPE). Resulting model outperforms the previously existing models significantly with its main competency being in commercial product recommendations. It is a fast, scalable, and efficient in ill-posed cases, including short text and sparse data. The model is trained and tested on well-known MovieLens 20M and NETFLIX datasets, and empirical evaluations such as recall, precision, sparsity and topic interpretations are promising.
Keywords: Machine learning|Probabilistic models|Recommender system|Topic models; strojové učení|pravděpodobnostní modely|doporučovací systémy

Institution: Charles University Faculties (theses) (web)
Document availability information: Available in the Charles University Digital Repository.
Original record: http://hdl.handle.net/20.500.11956/175638

Permalink: http://www.nusl.cz/ntk/nusl-510570


The record appears in these collections:
Universities and colleges > Public universities > Charles University > Charles University Faculties (theses)
Academic theses (ETDs) > Master’s theses