Sustav za preporučivanje video sadržaja na mobilnim uređajima

undergraduate thesis

• Title (english): Video recommendation system for mobile devices
• Author: Matej Šerbec
• Mentor: Ante Đerek (mentor)
• Committee member: Ante Đerek (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: 2015-07-13, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

U današnje vrijeme sustavi za upravljanje video sadržajem prisutni su u različitim domenama primjene. Upravo zbog povećanja korištenja takvih sustava i sveopćom idejom da se poboljša iskustvo krajnjeg korisnika i zadrži njegova zainteresiranost uz efikasnije konzumiranje video sadržaja, javlja se potreba za uvođenjem sustava za preporučivanje. U ovom radu dan je osvrt na različite tehnike preporučivanja uz praktičan primjer sustava za preporučivanje video sadržaja koji koristi hibridno težinsko preporučivanje kombiniranjem preporučivanja na temelju sadržaja i korisnika uz potporu NoSQL baze podataka MongoDB.

Abstract (english)

Nowdays, video content and recommendation is present in a variety of application domains. Because of the increasing use of such systems and the general idea to improve the end user experience and keep his interest with the efficient consumption of video content, there is a need for the recommendation engine. In this paper, there is a review of the different recommendation techniques and practical example of the recommender system that uses a weighted hybrid recommender combining collaborative filtering and content-based recommendations with support of the NoSQL database MongoDB.

• Keywords: swift; iOS; sustav preporučivanja; kolaborativno preporučivanje; preporučivanje na temelju sadržaja; hibridno preporučivanje; NodeJS; MongoDB
• Keywords (english): swift; iOS; recommender system; collaborative filtering; content-based recommendation; hybrid recommendation; NodeJS; MongoDB
• Language: croatian
• URN:NBN: urn:nbn:hr:168:927586
• 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: 2018-02-06 12:15:34