The Use of Hypergraph for Collaborative Filtering Recommendation Method

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12510%2F24%3A43909548" target="_blank" >RIV/60076658:12510/24:43909548 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10712622" target="_blank" >https://ieeexplore.ieee.org/document/10712622</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACIT62333.2024.10712622" target="_blank" >10.1109/ACIT62333.2024.10712622</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The Use of Hypergraph for Collaborative Filtering Recommendation Method

Popis výsledku v původním jazyce

Currently, the number of users using online platforms to purchase various products and services is increasing. Recommender systems allow users to find easier products and services based on their preferences and tastes. This paper presents a collaborative recommendation system using a hypergraph network structure. These systems create a profile of the target user using their similarity to some other ones. It is the reason, the collaborative recommendation method is susceptible to the measure of similarity which is utilized to express the dependence among users and products (products). To better express the interlinkages among users and products in the recommender system, we suggest a recommendation algorithm for collaborative filtering which is based on the similarity measure in hypergraphs and further using the clustering method. Using a hypergraph to model user interactions with products makes it possible to analyze user groupings and create recommendations for individuals within each community. This paper introduces a hypergraph model for capturing complex relationships, describes the necessary algorithms, and ability to produce appropriate recommendations based on experimental data. Experiments were performed on two standard datasets (MovieLens100k and CiaoDVD). The results showed that the suggested method is applicable and gives comparable results to other collaborative recommendation algorithms.

Název v anglickém jazyce

The Use of Hypergraph for Collaborative Filtering Recommendation Method

Popis výsledku anglicky

Currently, the number of users using online platforms to purchase various products and services is increasing. Recommender systems allow users to find easier products and services based on their preferences and tastes. This paper presents a collaborative recommendation system using a hypergraph network structure. These systems create a profile of the target user using their similarity to some other ones. It is the reason, the collaborative recommendation method is susceptible to the measure of similarity which is utilized to express the dependence among users and products (products). To better express the interlinkages among users and products in the recommender system, we suggest a recommendation algorithm for collaborative filtering which is based on the similarity measure in hypergraphs and further using the clustering method. Using a hypergraph to model user interactions with products makes it possible to analyze user groupings and create recommendations for individuals within each community. This paper introduces a hypergraph model for capturing complex relationships, describes the necessary algorithms, and ability to produce appropriate recommendations based on experimental data. Experiments were performed on two standard datasets (MovieLens100k and CiaoDVD). The results showed that the suggested method is applicable and gives comparable results to other collaborative recommendation algorithms.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2024 14th International Conference on Advanced Computer Information Technologies ACIT&apos;2024 Conference Proceedings

ISBN

979-8-3503-5003-6

ISSN

2770-5218

e-ISSN

—

Počet stran výsledku

4

Strana od-do

284-287

Název nakladatele

IEEE

Místo vydání

České Budějovice

Místo konání akce

České Budějovice

Datum konání akce

19. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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