Improvement Graph Convolution Collaborative Filtering with Weighted Addition Input

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Improvement Graph Convolution Collaborative Filtering with Weighted Addition Input

Original language description

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).

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

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

Article name in the collection

INTELLIGENT INFORMATION AND DATABASE SYSTEMS, ACIIDS 2022, PT I

ISBN

978-3-031-21742-5

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

13

Pages from-to

635-647

Publisher name

SPRINGER INTERNATIONAL PUBLISHING AG

Place of publication

CHAM

Event location

Ho Chi Minh City

Event date

Nov 28, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000917075200050