CRTL: Context Restoration Transfer Learning for Cross-Domain Recommendations
| Autor: | Ming He, Jiuling Zhang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer Networks and Communications
Computer science business.industry Intelligent decision support system Negative transfer Context (language use) Recommender system Machine learning computer.software_genre Domain (software engineering) Artificial Intelligence Collaborative filtering Artificial intelligence Transfer of learning business computer Knowledge transfer |
| Zdroj: | IEEE Intelligent Systems. 36:65-72 |
| ISSN: | 1941-1294 1541-1672 |
| Popis: | Among different recommendation techniques, collaborative filtering usually suffers from limited performance due to the data sparsity problem. Transfer learning presents an unprecedented opportunity to alleviate this issue by transferring rating patterns from the source domain to the target domain. However, an issue occurs in some situations when the source and target domains have unrelated parts to each other, such that a negative transfer may reduce the learning performance in the target domain. To address this issue, in this article, we present a novel model called cluster rating bias restoration to divide the learning process into two stages. First, we employ traditional transfer learning methods to adapt and reconstruct the target rating matrix. Second, we propose a cluster rating bias restoration framework specifically to minimize the effect of the negative transfer from the source domain and to restore the context-information-based rating bias trend in the target domain. Experiments on real-world datasets show that our proposed model outperforms the state-of-the-art alternatives. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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