Ostvarenje sustava preporučivanja koristeći algoritam suradničkog filtriranja

undergraduate thesis

• Title (english): Recommendation Systems Implementation Using Collaborative Filtering Algorithm
• Author: Lucija Bago
• Mentor: Klemo Vladimir (mentor)
• Committee member: Klemo Vladimir (predsjednik povjerenstva)
• Committee member: Adrian Satja Kurdija (član povjerenstva)
• Committee member: Lucija Šikić (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2021-09-07, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

„A lot of times, people don’t know what they want until you show it to them“ ili u prijevodu „Ljudi često ne znaju što žele dok im to ne pokažeš“, citat je Stevea Jobsa koji savršeno opisuje ulogu sustava preporučivanja. Sustavi preporučivanja igraju veliku ulogu u današnjem svijetu, olakšavajući pristup brojnim sadržajima, produktima i uslugama koje ne bismo sami lako pronašli. Zaslužni su za individualne preporuke u moru različitih ponuda, pritom ne zatrpavajući korisnika beskorisnim informacijama već održavajući savršenu ravnotežu onoga što se nudi i onoga što pojedinom korisniku treba. Formalnije, sustavi preporučivanja su algoritmi koji služe za predlaganje sadržaja korisnicima na temelju njihovih prethodnih pretraživanja, njihovih afiniteta, opredjeljenja i stavova ili na temelju sličnosti s drugim korisnicima. Postoje dvije osnovne vrste sustava preporučivanja, oni temeljeni na sadržaju i oni temeljeni na suradničkom filtriranju.

Abstract (english)

As Steve Jobs once said, „A lot of times, people don’t know what they want until you show it to them“. The quote describes the role of recommender systems flawlessly.
Recommender systems have an extremely important role nowadays, facilitating access to the enormous amount of content, products, and services we wouldn't have found ourselves otherwise. They take credit for individual recommendations in a sea of diverse offers, perfectly balancing what is out there and what the user needs without overwhelming them with useless information. To be more formal, recommender systems are algorithms used to recommend content to users based on their previous searches, their preferences, orientation, perspectives, affiliations, or based on the similarity with other users. There are two main types of recommender systems, ones that are content-based and collaborative filtering recommender systems.

• Keywords: sustav preporučivanja; suradničko filtriranje; sustavi preporučivanja temeljeni na sadržaju; matrica korisnosti; fenomen dugog repa; problem hladnog starta; Pearsonov koeficijent korelacije; korisnički orijentirani sustavi preporučivanja; sustavi preporučivanja orijentirani produktu; susjedstvo korisnika; Apache Mahout
• Keywords (english): recommender system; collaborative filtering; content-based recommender systems; utility matrix; long tail phenomenon; cold start issue; Pearson correlation coefficient; user based recommender systems; item based recommender systems; user neighbourhood; Apache Mahout
• Language: croatian
• URN:NBN: urn:nbn:hr:168:792415
• Study programme: Title: Computing; Study programme type: university; Study level: undergraduate; Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-15 09:42:55