izpis_h1_title_alt

Priporočanje nastanitev z uporabo ponudnika strojnega učenja v oblaku
Slapničar, Gašper (Author), Bosnić, Zoran (Mentor) More about this mentor... This link opens in a new window

.pdfPDF - Presentation file, Download (3,39 MB)

Abstract
Priporočilni sistemi so vseprisotna tehnologija na spletu in lahko ključno vplivajo na poslovne rezultate podjetij. V diplomskem delu se soočimo z razvojem produkcijskega priporočilnega sistema za spletno stran, ki ponuja ekološke nastanitve, ki dosegajo določene ekološke standarde (npr. uporaba sončne energije, filtriranje in ponovna uporaba vode, recikliranje odpadkov itd.). Najprej pregledamo tehnologije velikih podatkovnih množic (angl. big data) in ponudnike strojnega učenja v oblaku. Nato izberemo najustreznejšo platformo in jo uporabimo za zbiranje podatkov in razvoj priporočilnega sistema, ki vrača priporočila za uporabnika z uporabo algoritma matrične faktorizacije (angl. Alternating Least Squares, ALS) ter obenem vrača tudi podobne priporočilne objekte z uporabo Jaccardove podobnosti in evklidske razdalje. Na koncu sistem interno ocenimo na že zbranih podatkih z uporabo statistične mere Precision@k. Rezultati evalvacije so pokazali 19% točnost napovedi, kar je bistveno boljše od naključnega priporočanja, ki doseže 1% točnost. Predlagamo tudi možno implementacijo na spletni strani z namenom izboljšanja poslovnih rezultatov.

Language:Slovenian
Keywords:priporočilni sistem, vzporedno računanje, strojno učenje, velike podatkovne množice, matrična faktorizacija
Work type:Bachelor thesis/paper (mb11)
Organization:FRI - Faculty of computer and information science
Year:2015
Views:733
Downloads:356
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:Recommending accommodations using machine learning provider in a cloud
Abstract:
Recommender systems are present almost everywhere on the web and can be the key to potentially improved business results. In this thesis we develop a production-ready recommender system for a website that offers eco-sustainable accommodations, that meet certain requirements (e.g. usage of solar energy, water filtering and reuse, waste recycling etc.). First we examine crucial big data technologies and some of the cloud-based machine learning platforms. We proceed to choose the best platform and use it to collect data and develop a recommender system, which returns predictions for a user, based on a matrix factorization algorithm (Alternating Least Squares, ALS). It also returns similar items based on Jaccard similarity and euclidian distance. We conclude with system evaluation by using Precision@k statistical measure. The evaluation results have shown 19% precision accuracy, which greatly exceeds the results of random recommendation that achieves 1% precision accuracy. We also propose a potential website implementation with the intention of improving business results.

Keywords:recommender system, parallel computing, machine learning, big data, matrix factorization

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