Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Computing | en_US |
| dc.contributor.advisor | Li, Qing (COMP) | en_US |
| dc.creator | Wu, Jiahao | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/14414 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Data-centric approaches for advanced and efficient recommendation modeling | en_US |
| dcterms.abstract | Recommender systems have become one of the most successful applications of artificial intelligence's in daily life, significantly relieving people from overwhelming information load and assisting users in decision-making by providing personalized recommendations. Advanced recommendation modeling techniques have been deployed across various platforms, i.e., Tiktok in video recommendations, Linkedin in friend recommendations, Goodreads for book recommendations, and Taobao for item recommendations. | en_US |
| dcterms.abstract | Despite their widespread success, existing recommendation approaches face several limitations, particularly in terms of multi-faceted user modeling and scalability. For instance, these models often experience performance degradation when handling multi-faceted user data. Additionally, many state-of-the-art recommendation models are trained on large-scale datasets, making the training process both computationally expensive and inefficient, especially when fine-tuning hyperparameters or architectures to optimize recommendation performance. To address these challenges, this dissertation adopts a data-centric perspective, proposing curated data manipulation and optimization strategies to enhance the performance of recommendation models across different scenarios. | en_US |
| dcterms.abstract | Firstly, we demonstrate that disentangling various facets of data (i.e., social data and user-item data) for independent modeling can significantly improve recommendation performance. Furthermore, we explore the potential to substantially reduce the size of user-item interaction and content data in recommendation datasets while preserving critical information, thus markedly decreasing training costs. Different from model-centric approaches that are often tailored to specific recommendation models, data-centric strategies generate enhanced datasets that offer benefits across a range of existing models. By addressing key challenges related to personalized modeling and computational efficiency from a perspective of data-centric, this dissertation lays the foundation for next-generation recommendation techniques. | en_US |
| dcterms.extent | xvi, 126 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2026 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.accessRights | open access | en_US |
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