Improvement Graph Convolution Collaborative Filtering with Weighted Addition Input

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10251993" target="_blank" >RIV/61989100:27240/22:10251993 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-21743-2_51" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-21743-2_51</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-21743-2_51" target="_blank" >10.1007/978-3-031-21743-2_51</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improvement Graph Convolution Collaborative Filtering with Weighted Addition Input

Popis výsledku v původním jazyce

Graph Neural Networks have been extensively applied in the field of machine learning to find features of graphs, and recommendation systems are no exception. The ratings of users on considered items can be represented by graphs which are input for many efficient models to find out the characteristics of the users and the items. From these insights, relevant items are recommended to users. However, user&apos;s decisions on the items have varying degrees of effects on different users, and this information should be learned so as not to be lost in the process of information mining. In this publication, we propose to build an additional graph showing the recommended weight of an item to a target user to improve the accuracy of GNN models. Although the users&apos; friendships were not recorded, their correlation was still evident through the commonalities in consumption behavior. We build a model WiGCN (Weighted input GCN) to describe and experiment on well-known datasets. Conclusions will be stated after comparing our results with state-of-the-art such as GCMC, NGCF and LightGCN. The source code is also included (https://github.com/trantin84/WiGCN).

Název v anglickém jazyce

Improvement Graph Convolution Collaborative Filtering with Weighted Addition Input

Popis výsledku anglicky

Graph Neural Networks have been extensively applied in the field of machine learning to find features of graphs, and recommendation systems are no exception. The ratings of users on considered items can be represented by graphs which are input for many efficient models to find out the characteristics of the users and the items. From these insights, relevant items are recommended to users. However, user&apos;s decisions on the items have varying degrees of effects on different users, and this information should be learned so as not to be lost in the process of information mining. In this publication, we propose to build an additional graph showing the recommended weight of an item to a target user to improve the accuracy of GNN models. Although the users&apos; friendships were not recorded, their correlation was still evident through the commonalities in consumption behavior. We build a model WiGCN (Weighted input GCN) to describe and experiment on well-known datasets. Conclusions will be stated after comparing our results with state-of-the-art such as GCMC, NGCF and LightGCN. The source code is also included (https://github.com/trantin84/WiGCN).

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

INTELLIGENT INFORMATION AND DATABASE SYSTEMS, ACIIDS 2022, PT I

ISBN

978-3-031-21742-5

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

13

Strana od-do

635-647

Název nakladatele

SPRINGER INTERNATIONAL PUBLISHING AG

Místo vydání

CHAM

Místo konání akce

Ho Chi Minh City

Datum konání akce

28. 11. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000917075200050