SGCF: Inductive Movie Recommendation System with Strongly Connected Neighborhood Sampling

SGCF: Inductive Movie Recommendation System with Strongly Connected Neighborhood Sampling

recommendation system, collaborative filtering, graph neural network

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.

Jatmiko Budi Baskoro, Evi Yulianti

http://dx.doi.org/10.21609/jiki.v15i1.1066

Faculty of Computer Science Universitas Indonesia

022-02-27

Sri Wahyuni

e-ISSN : 2502-9274 printed ISSN : 2088-7051

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Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)