Benchmarking Deep Neural Networks for Modern Recommendation Systems
This paper evaluates the performance of 7 neural network architectures on 3 recommendation datasets, analyzing their effectiveness in accuracy, recall, F1-score, and diversity.
Why it matters
This benchmarking study provides insights into the strengths and limitations of different deep learning architectures for modern recommendation systems, informing future advancements in the field.
Key Points
- 1Compares CNN, RNN, GNN, Autoencoder, Transformer, NCF, and Siamese Networks on Retail, Amazon, and Netflix datasets
- 2GNNs excel at managing complex item relationships in e-commerce, RNNs capture temporal dynamics for platforms like Netflix
- 3Siamese Networks contribute to diversifying recommendations, especially in retail
- 4Challenges include computational demands, reliance on data, and balancing accuracy vs. diversity
Details
The paper examines the deployment of 7 different neural network architectures - CNN, RNN, GNN, Autoencoder, Transformer, NCF, and Siamese Networks - on 3 distinct recommendation datasets: Retail E-commerce, Amazon Products, and Netflix Prize. It evaluates their effectiveness through metrics like accuracy, recall, F1-score, and diversity in recommendations. The results show that GNNs are particularly adept at managing complex item relationships in e-commerce environments, while RNNs are effective in capturing the temporal dynamics essential for platforms like Netflix. Siamese Networks are highlighted for their contribution to diversifying recommendations, especially in retail settings. However, the study also addresses issues like high computational demands, reliance on extensive data, and the challenge of balancing accurate and diverse recommendations. The paper suggests exploring hybrid methods that merge the strengths of various models to better satisfy user preferences and accommodate the evolving needs of contemporary digital platforms.
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