Review-based hierarchical attention cooperative neural networks for recommendation
| Autor: | Yongping Du, Lulin Wang, Zhi Peng, Wenyang Guo |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Information retrieval Artificial neural network Computer science Cognitive Neuroscience 02 engineering and technology Preference Computer Science Applications 020901 industrial engineering & automation Artificial Intelligence Entity–relationship model 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Embedding 020201 artificial intelligence & image processing Representation (mathematics) |
| Zdroj: | Neurocomputing. 447:38-47 |
| ISSN: | 0925-2312 |
| Popis: | In e-commerce platform, users conduct purchase behavior and write reviews for the purchased items. These reviews usually contain a lot of valuable information for recommendation, which can reflect the purchase preference of the user and the characteristic of the item. We propose the Hierarchical Attention Cooperative Neural Networks (HACN) model for recommendation. Hierarchical attention mechanism is adopted to enrich user’s and item’s feature representation from review texts. Two parallel networks based on review texts are used to model users and items respectively, which makes the generated features more purposeful. Further, the target ID embedding is introduced to capture the global entity relationship in the dataset. The experiments are performed on five real-world datasets of different domains from Amazon, and our proposed HACN model has achieved better results than the existing state-of-the-art methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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