diplomsko delo
Barbara Tvrdi (Avtor), Blaž Zupan (Mentor)

Povzetek

Uporaba dejavnikov optimizacije spletnih strani za napovedovanje uvrstitev v iskalniku Google

Ključne besede

spletni iskalniki;optimizacija spletnih strani;strojno učenje;računalništvo;univerzitetni študij;diplomske naloge;

Podatki

Jezik: Slovenski jezik
Leto izida:
Tipologija: 2.11 - Diplomsko delo
Organizacija: UL FRI - Fakulteta za računalništvo in informatiko
Založnik: [B. Tvrdi]
UDK: 004(043.2)
COBISS: 9344340 Povezava se bo odprla v novem oknu
Št. ogledov: 46
Št. prenosov: 2
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Angleški jezik
Sekundarni naslov: Use of search engine optimization factors for Google page rank prediction
Sekundarni povzetek: Over the years, search engines have become an important tool for finding information. It is known that users select the link on the first page of search results in 62% of the cases. Search engine optimization techniques enable website improvement and therefore a better ranking in search engines. The exact specification of the factors that affect website ranking is not disclosed by search engine owners. In this thesis we tried to choose some most frequently mentioned search engine optimization factors for Google search engine. Using the factors we tried to apply machine learning methods to build a model that predicts whether a site would be ranked among the top 10 search results (i.e. the first page of search engine results). The best results were achieved using a classification method called random forests, but the obtained AUC was below acceptable AUC estimates for such problems. We also tried to find statistically significant informative features. Only a few features matched the criteria, but had a very low information content. To achieve better results other features could be used and the number of training examples could be increased.
Sekundarne ključne besede: search engines;search engine optimization;machine learning;computer science;diploma;
Vrsta datoteke: application/pdf
Vrsta dela (COBISS): Diplomsko delo
Komentar na gradivo: Univ. v Ljubljani, Fak. za računalništvo in informatiko
Strani: 39 str.
ID: 24142525