Hibridni sustav preporuka za filmove

master's thesis

• Title (english): Hybrid Recommender System For Movies
• Author: Luka Mandić
• Mentor: Marin Šilić (mentor)
• Committee member: Marin Šilić (predsjednik povjerenstva)
• Committee member: Goran Delač (član povjerenstva)
• Committee member: Klemo Vladimir (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2023-07-04, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Diplomski rad opisuje model preporučiteljskog sustava zasnovanog na sadržajnim značajkama i model preporučiteljskog sustava zasnovanog na suradničkom filtriranju. U modelu zasnovanom na sadržajnim značajkama definiraju se profil proizvoda, profil korisnika i funkcija sličnosti. U modelu zasnovanom na suradničkom filtriranju definiraju se susjedstvo, funkcija sličnosti i izračun predviđanja. Uvodi se hibridni model za preporuku filmova. Osnova modela je model zasnovan na sadržajnim značajkama, a unaprjeđuje se modelom zasnovanom na suradničkom filtriranju. Hibridni model umanjuje nedostatke: „hladni start“ (engl. Cold start) i pre-specijaliziranost, te koristi prednosti za bolju preporuku filmova.
Pročišćava se i prilagođava skup podataka MovieLens. MovieLens sadrži 1,000,209 korisničkih ocjena filmova, te podatke o 3,900 filmova. Programski se ostvaraje model za preporuku filmova zasnovan na sadržajnim značajkama. Računa se točnost modela. Programski se ostvaruje hibridni model za preporuku filmova. Računa se točnost modela i uspoređuje se sa točnosti modela zasnovanog na sadržajnim značajkama. Komentiraju se rezultati.

Abstract (english)

The final thesis presents a comprehensive exploration of two recommender system models: a content-based model and a collaborative filtering model. The content-based model encompasses the definition of product and user profiles, as well as the establishment of a similarity function. The collaborative filtering model introduces the concept of neighborhood, similarity function, and prediction calculation.
To enhance the recommendation process for movies, a hybrid model is introduced, building upon the foundation of the content-based model and incorporating the strengths of collaborative filtering. This hybrid approach effectively addresses challenges such as the cold start problem and over-specialization, resulting in improved movie recommendations.
The MovieLens dataset is carefully refined and adapted to ensure its suitability for the study. This dataset encompasses a wealth of information, including 1,000,209 user ratings for movies and data pertaining to 3,900 films.
Through the implementation of a programmatic movie recommendation model based on content features, the accuracy of the model is evaluated. Additionally, a hybrid model for movie recommendation is implemented, and its accuracy is compared to that of the content-based model. Finally, the results are thoroughly examined and discussed to provide valuable insights and conclusions.

• Keywords: preporučivanje; sustav za preporučivanje; hibridni sustav za preporučivanje filmova; sustav za preporučivanje zasnovan na sadržajnim značajkama; suradničko filtriranju; profil proizvoda; profil korisnika; funkcija sličnosti; susjedstvo; Movie Lens
• Keywords (english): hybrid movie recommendation system; movie recommendations; content-based model; collaborative filtering-based model; product profile; user profile; similarity function; neighborhood; cold start problem; over-specialization; MovieLens dataset
• Language: croatian
• URN:NBN: urn:nbn:hr:168:361466
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-10-11 09:38:59