The Use of Hypergraph for Collaborative Filtering Recommendation Method
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
The Use of Hypergraph for Collaborative Filtering Recommendation Method
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2024 14th International Conference on Advanced Computer Information Technologies ACIT'2024 Conference Proceedings
ISBN
979-8-3503-5003-6
ISSN
2770-5218
e-ISSN
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Number of pages
4
Pages from-to
284-287
Publisher name
IEEE
Place of publication
České Budějovice
Event location
České Budějovice
Event date
Sep 19, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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