izpis_h1_title_alt

Časovno pogojen priporočilni sistem za oglaševanje
ŠUŠTERŠIČ, JAN (Author), Bosnić, Zoran (Mentor) More about this mentor... This link opens in a new window, Košir, Domen (Co-mentor)

.pdfPDF - Presentation file, Download (2,40 MB)

Abstract
V zadnjih dveh desetletjih se je zaradi velike porasti uporabe interneta zelo razširilo spletno oglaševanje, kjer je eden izmed največkrat uporabljenih pokazateljev uspešnosti stopnja interakcije oglasov, ki je odvisna od številnih dejavnikov. V diplomski nalogi se osredotočamo na napovedovanje stopnje interakcije z oglasi z uporabo priporočilnih sistemov, ki temeljijo na matrični faktorizaciji. Pri tem poskušamo napovedi osnovne matrične faktorizacije izboljšati z upoštevanjem časovnih podatkov o uri in datumu, iz katerih lahko razberemo starost zapisa in kontekst, v katerem je bil ta zabeležen. Zgrajen časovno pogojeni priporočilni sistem primerjamo s statičnimi modeli in analiziramo stopnjo izboljšanja. Rezultati so pokazali, da z uporabo podatkov o starosti zapisov na dani množici podatkov konsistentno izboljšamo napovedi, medtem ko uporaba kontekstnih podatkov pripelje do slabših rezultatov in zahteva nadaljnje raziskave.

Language:Slovenian
Keywords:oglaševanje, priporočilni sistemi, matrična faktorizacija, čas
Work type:Bachelor thesis/paper (mb11)
Organization:FRI - Faculty of computer and information science
Year:2020
COBISS.SI-ID:1538539459 Link is opened in a new window
Views:318
Downloads:114
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:Time aware recommender system for advertising
Abstract:
Due to the vast increase in the usage of internet in the past two decades there has been a major increase in the popularity of online advertising where one of the most widely used success indicators is ad interaction rate which depends upon multiple factors. The thesis focuses on predicting ad interaction rate with the use of recommender systems that are based on matrix factorization. We try to improve the basic matrix factorization by incorporating time data such as age and context into our recommendations. We then compare the time-aware recommender system with its more basic counterpart and analyse performance improvements. Results show that the use of information such as age can consistently improve recommendations on our dataset. Context information however, produces worse results and requires further research.

Keywords:advertisement, recommender systems, matrix factorization, time

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