1 research outputs found
μ€ννΈλ΄ μ΄μ€ κ΅°μ§νλ₯Ό μ΄μ©ν κ·ΈλνκΈ°λ° νμ νν°λ§μ κ΅μ§ μμλΈ λ°©λ²
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Όλ¬Έ(μμ¬) -- μμΈλνκ΅λνμ : μμ°κ³Όνλν νλκ³Όμ κ³μ°κ³Όνμ 곡, 2022. 8. κ°λͺ
μ£Ό.The importance of a personalized recommendation system is emerging as the world becomes more complex and individualized. Among various recommendation systems, Neural Graph Collaborative Filtering(NGCF) and its variants treat the user-item set as a bipartite graph and learn the interactions between user and item without using their unique features. While these approaches only using collaborative signals have achieved state-of-the-art performance, they still have the disadvantage of abandoning feature similarity among users and items. To tackle this problem, we adopt unsupervised community detection from bipartite graph structure to enhance the collaborative signal for a Graph-based recommendation system. Co-Clustering algorithms segment the user-item matrix into small groups. Each local CF captures a strong correlation among these local user-item subsets, while the original incidence matrix is also used to analyze global interaction between groups. Finally, our Local-Ensemble Graph Collaborative Filtering(LEGCF) aggregates all local and global collaborative information. As the proposed approach can utilize various Co-clustering and Collaborative Filtering flexibly, one of the most straightforward variants, Spectral Co-Clustering and NGCF, can enhance the overall performance.λ³Έ λ
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νν°λ§ λͺ¨λΈμ λνμ¬ μΆμ² λ₯λ ₯μ΄ ν₯μλμλ€1 Introduction 1
2 Preliminaries 5
2.1 Spectral Co-Clustering 5
2.1.1 Bipartite Graph Partitioning 5
2.1.2 Optimization 8
2.2 Bayesian Personalized Ranking(BPR) Loss 11
2.2.1 Implicit Data 11
2.2.2 Personalized Total Ranking 11
2.2.3 Bayesian Personalized Ranking 12
3 Proposed Method 15
3.1 Dataset 15
3.2 Spectral Co-Clustering 15
3.3 Local-Ensemble model 17
4 Experimental Result 20
4.1 Evaluation Metric 20
4.2 Result Analysis 21
5 Conclusion 29
References 31
Abstract (in Korean) 35μ