izpis_h1_title_alt

Primerjava latentnih modelov za priporočilne sisteme v strojnem učenju
BUCIK, DEMIAN (Author), Bosnić, Zoran (Mentor) More about this mentor... This link opens in a new window

.pdfPDF - Presentation file, Download (1,03 MB)
MD5: 0AF04C50CDFAB247CA2002B3A7FED081

Abstract
Vseprisotnost priporočilnih sistemov v digitalnem ekosistemu korenito spreminja način uporabe spletnih storitev. V poplavi informacij se uporabniki za dostop do zanimivih in koristnih vsebin zanašajo na njihova priporočila. Cilj te diplomske naloge je predstaviti dve vrsti priporočilnih sistemov, ki temeljijo na skupinskem filtriranju. Pri konstrukciji modelov se opremo na določene koncepte strojnega učenja. Modele implementiramo v okolju Python in evalviramo pri napovedovanju števila poslušanj izvajalcev in uporabnikovih ocen vsebine, za kar uporabimo dve podatkovni množici. Ob tem predlagamo način razširitve omejenega Boltzmannovega stroja za priporočanje in modeliranje zveznih ocen ter uporabimo dodatek modelu na osnovi matričnega razcepa, ki omogoča modeliranje prijateljstev med uporabniki. Rezultati nakazujejo, da modeli na osnovi matričnega razcepa delujejo občutno bolje in da se splača več časa investirati v njihov nadaljnji razvoj.

Language:Slovenian
Keywords:skupinsko filtriranje, strojno učenje, matrična faktorizacija, omejen Boltzmannov stroj, metoda največje aposteriorne verjetnosti
Work type:Bachelor thesis/paper (mb11)
Organization:FRI - Faculty of computer and information science
Year:2019
Views:348
Downloads:186
Metadata:XML RDF-CHPDL DC-XML DC-RDF
 
Average score:(0 votes)
Your score:Voting is allowed only to logged in users.
:
Share:AddThis
AddThis uses cookies that require your consent. Edit consent...

Secondary language

Language:English
Title:Comparison of latent models for recommender systems in machine learning
Abstract:
The ubiquity of recommender systems in the digital ecosystem is thoroughly changing the way web services are used. In the ceaseless stream of information, users are relying on their recommendations to access interesting and useful content. The aim of this thesis is to present two approaches to collaborative filtering based recommender systems. Developing the models, we borrow certain concepts from the field of machine learning. All models are implemented in Python and evaluated on two datasets, one containing the numbers of plays and the other containing users' ratings of content. We propose a way to model continuous ratings with restricted Boltzmann machine and apply an addition to the matrix factorization model that makes it possible to model friendships between users. The results suggest that matrix factorization models work considerably better and that more time should be invested in their further development.

Keywords:collaborative filtering, machine learning, matrix factorization, restricted Boltzmann machine, maximum a posteriori estimation

Similar documents

Similar works from RUL:
Similar works from other Slovenian collections:

Comments

Leave comment

You have to log in to leave a comment.

Comments (0)
0 - 0 / 0
 
There are no comments!

Back