Enorazredni priporočilni sistemi

Povzetek

Ključne besede

Podatki

Jezik:	Slovenski jezik
Leto izida:	2011
Tipologija:	2.11 - Diplomsko delo
Organizacija:	UL FRI - Fakulteta za računalništvo in informatiko
Založnik:	[M. Perovšek]
UDK:	004(043.2)
COBISS:	8690516
Št. ogledov:	52
Št. prenosov:	5
Ocena:	0 (0 glasov)
Metapodatki:

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Sekundarni jezik:	Angleški jezik
Sekundarni naslov:	Single-class recommendation systems
Sekundarni povzetek:	The task of recommender systems is to recommend items that fit the user's preferences. Recommender systems are today often used in web applications and shops in order to help the user in selecting and purchasing items from an overwhelming set of choices. The data from where the hidden preference criteria can be learned often only contains single-class values (web links clicks, bookmarks ...) instead of elaborative ranking. Such data is comprised of only positive examples, listing items that the user has liked or has expressed interest for. For other items, the preference is unknown and may be positive or negative. In this work we study the recommender algorithms that can learn from such data. We examined two types of algorithms. First, RISMF and wALS algorithms, are based on matrix factorization which identifies and later recommends items based on relationships between users and items. We also proposed two types of nearest neighbours algorithms: user and item based. We implemented all of the algorithms and performed a comparison on CiteULike.org website’s database. Results show that wALS algorithms give the best results on the selected data.
Sekundarne ključne besede:	recommender systems;one-class data;collaborative filtering;matrix factorization;k-Nearest neighbours methods;computer science;diploma;
Vrsta datoteke:	application/pdf
Vrsta dela (COBISS):	Diplomsko delo
Komentar na gradivo:	Univ. v Ljubljani, Fak. za računalništvo in informatiko
Strani:	56 str.
ID:	24093596