Earticle

초록

영어

Bayesian Personalized Ranking (BPR) assigns ranks to a set of items to recommend them to users. This study proposes a novel approach to improve the performance of recommendation systems. The first proposed method for the enhanced recommendation system constructs positive preferences by utilizing only the items explicitly preferred by users rather than treating all interacted items (e.g., clicked, rated, or reviewed) as positive, as was traditionally done. The second method involves using explicitly non-preferred items as negative data, and the traditional approach of using only noninteracted items as negative. Message propagation is performed on subgraphs generated through meta-path design. The results of each subgraph are used to learn the representations of users and items through an attention mechanism and graph representation learning based on this data configuration. The system then predicts scores for user-item pairs. For evaluation, the performance of the recommendation system was assessed using not only traditional accuracy metrics but also by defining a pairwise ranking accuracy metric. Pairwise ranking accuracy assigns ranks to preferred and non-preferred items to determine if the model reflects user preferences. Experimental results showed improved performance in widely used evaluation metrics for recommendation systems, such as Hit Rate and Normalized Discounted Cumulative Gain, and higher performance in pairwise ranking accuracy.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
III. DATASET
A. Data Filtering
IV. EXPERIMENT
A. Sampling Strategy
B. Train Data Composition
C. Train Model
D. Evaluation Metrics
E. Test Data Composition
V. RESULT
VI. CONCLUSION
ACKNOWLEGEMENT
REFERENCES

저자

• Jeongho Kim [ Department of Information Communication Engineering Myongji University Yongin, South Korea ]
• Jaehee Jung [ Department of Information Communication Engineering Myongji University Yongin, South Korea ] Corresponding Author

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