Attention-Based High-Order Feature Interactions to Enhance the Recommender System for Web-Based Knowledge-Sharing Service
| Autor: | Jun Shen, Ghassan Beydoun, Li Li, Wei Wei, Tingru Cui, David E. Pritchard, Geng Sun, Jiayin Lin, Shiping Chen, Dongming Xu |
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| Přispěvatelé: | Huang, Z, Beek, W, Wang, H, Zhou, R, Zhang, Y |
| Rok vydání: | 2020 |
| Předmět: |
Service (systems architecture)
Computer science business.industry Big data Context (language use) 02 engineering and technology Recommender system computer.software_genre Information overload Knowledge sharing World Wide Web 020204 information systems 0202 electrical engineering electronic engineering information engineering Web application Artificial Intelligence & Image Processing 020201 artificial intelligence & image processing Web service business computer |
| Zdroj: | Web Information Systems Engineering – WISE 2020 ISBN: 9783030620042 WISE (1) |
| DOI: | 10.1007/978-3-030-62005-9_33 |
| Popis: | Providing personalized online learning services has become a hot research topic. Online knowledge-sharing services represents a popular approach to enable learners to use fragmented spare time. User asks and answers questions in the platform, and the platform also recommends relevant questions to users based on their learning interested and context. However, in the big data era, information overload is a challenge, as both online learners and learning resources are embedded in data rich environment. Offering such web services requires an intelligent recommender system to automatically filter out irrelevant information, mine underling user preference, and distil latent information. Such a recommender system needs to be able to mine complex latent information, distinguish differences between users efficiently. In this study, we refine a recommender system of a prior work for web-based knowledge sharing. The system utilizes attention-based mechanisms and involves high-order feature interactions. Our experimental results show that the system outperforms known benchmarks and has great potential to be used for the web-based learning service. |
| Databáze: | OpenAIRE |
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