Bi-preference Learning Heterogeneous Hypergraph Networks for Session-based Recommendation

X Zhang, B Xu, Z Ren, X Wang, H Lin… - Proceedings of the 47th …, 2024 - dl.acm.org

Session-based recommendation aims to predict intents of anonymous users based on their
limited behaviors. Modeling user behaviors involves two distinct rationales: co-occurrence …

被引用次数：3 相关文章所有 2 个版本

[PDF] arxiv.org

FineRec: Exploring Fine-grained Sequential Recommendation

X Zhang, B Xu, Y Wu, Y Zhong, H Lin… - Proceedings of the 47th …, 2024 - dl.acm.org

Sequential recommendation is dedicated to offering items of interest for users based on their
history behaviors. The attribute-opinion pairs, expressed by users in their reviews for items …

被引用次数：1 相关文章所有 2 个版本

[PDF] arxiv.org

Side Information-Driven Session-based Recommendation: A Survey

X Zhang, B Xu, C Li, Y Zhou, L Li, H Lin - arXiv preprint arXiv:2402.17129, 2024 - arxiv.org

The session-based recommendation (SBR) garners increasing attention due to its ability to
predict anonymous user intents within limited interactions. Emerging efforts incorporate …

被引用次数：2 相关文章所有 3 个版本

[PDF] arxiv.org

Don't Click the Bait: Title Debiasing News Recommendation via Cross-Field Contrastive Learning

Y Shu, X Zhang, Y Wu, B Xu, L Yang, H Lin - arXiv preprint arXiv …, 2024 - arxiv.org

News recommendation emerges as a primary means for users to access content of interest
from the vast amount of news. The title clickbait extensively exists in news domain and …

Integrating Multi-view Analysis: Multi-view Mixture-of-Expert for Textual Personality Detection

H Zhu, X Zhang, J Lu, L Yang, H Lin - arXiv preprint arXiv:2408.08551, 2024 - arxiv.org

Textual personality detection aims to identify personality traits by analyzing user-generated
content. To achieve this effectively, it is essential to thoroughly examine user-generated …