SGCF: Inductive Movie Recommendation System with Strongly Connected Neighborhood Sampling

Baskoro, Jatmiko Budi; Yulianti, Evi

doi:10.21609/jiki.v15i1.1066

Jurnal SINTA 2

SGCF: Inductive Movie Recommendation System with Strongly Connected Neighborhood Sampling

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 15 No. 1 (2022) · pp. 55–67

DOI: 10.21609/jiki.v15i1.1066
ISSN: 2502-9274, 2088-7051
Published: 2022-02-27
Publisher: Faculty of Computer Science - Universitas Indonesia
Language: eng

Abstract

User and item embeddings are key resources for the development of recommender systems. Recent works has exploited connectivity between users and items in graphs to incorporate the preferences of local neighborhoods into embeddings. Information inferred from graph connections is very useful, especially when interaction between user and item is sparse. In this paper, we propose graphSAGE Collaborative Filtering (SGCF), an inductive graph-based recommendation system with local sampling weight. We conducted an experiment to investigate recommendation performance for SGCF by comparing its performance with baseline and several SGCF variants in Movielens dataset, which are commonly used as recommendation system benchmark data. Our experiment shows that weighted SGCF perform 0.5% higher than benchmark in NDCG@5 and NDCG@10, and 0.8% in NDCG@100. Weighted SGCF perform 0.79% higher than benchmark in recall@5, 0.4% increase for recall@10 and 1.85% increase for recall@100. All the improvements are statistically significant with p-value < 0.05.

Keywords

Collaboration network of the authors

11 authors · 21 co-authorship links · 9 across different organisations

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University of Indonesia
Institut Teknologi Bacharuddin Jusuf Habibie
Taylor's University

Different organisations
Same organisation
Affiliation unknown
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Co-author	Organisation	Joint articles
Muhammad Rizki Syazali	University of Indonesia	1
Nicholas Pangestu	University of Indonesia	1
Meganingrum Arista Jiwanggi	University of Indonesia	1
Nuzulul Khairu Nissa	University of Indonesia	1
Naradhipa Bhary	University of Indonesia	1
Jafar Abdurrohman	University of Indonesia	1
Fariz Wahyuzan Dwitilas	University of Indonesia	1
Eka Qadri Nuranti	Institut Teknologi Bacharuddin Jusuf Habibie	1
Husna Sarirah Husin	Taylor's University	1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://jiki.cs.ui.ac.id/index.php/jiki/oai, first harvested 26 Okt 2026, last seen 1 Nov 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en-US">SGCF: Inductive Movie Recommendation System with Strongly Connected Neighborhood Sampling</dc:title>
	<dc:creator>Baskoro, Jatmiko Budi</dc:creator>
	<dc:creator>Yulianti, Evi</dc:creator>
	<dc:subject xml:lang="en-US">recommendation system</dc:subject>
	<dc:subject xml:lang="en-US">collaborative filtering</dc:subject>
	<dc:subject xml:lang="en-US">graph neural network</dc:subject>
	<dc:description xml:lang="en-US">User and item embeddings are key resources for the development of recommender systems. Recent works has exploited connectivity between users and items in graphs to incorporate the preferences of local neighborhoods into embeddings. Information inferred from graph connections is very useful, especially when interaction between user and item is sparse. In this paper, we propose graphSAGE Collaborative Filtering (SGCF), an inductive graph-based recommendation system with local sampling weight. We conducted an experiment to investigate recommendation performance for SGCF by comparing its performance with baseline and several SGCF variants in Movielens dataset, which are commonly used as recommendation system benchmark data. Our experiment shows that weighted SGCF perform 0.5% higher than benchmark in NDCG@5 and NDCG@10, and 0.8% in NDCG@100. Weighted SGCF perform 0.79% higher than benchmark in recall@5, 0.4% increase for recall@10 and 1.85% increase for recall@100. All the improvements are statistically significant with p-value &amp;lt; 0.05.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2022-02-27</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 15 No. 1 (2022): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 55-67</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
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	<dc:language>eng</dc:language>
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