output
20162025
most citedNeural Rating Regression with Abstractive Tips Generation for Recommendation

306 citations

Showing 2024Show all

5 papers · 1 filter

cs.LG202413 cited

Personalized Federated Continual Learning via Multi-granularity Prompt

Hao Yu, Xin Yang, Xin Gao +4

Personalized Federated Continual Learning (PFCL) is a new practical scenario that poses greater challenges in sharing and personalizing knowledge. PFCL not only relies on knowledge…

cs.NI202442 cited

Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning

Tianfu Wang, Li Shen, Qilin Fan +3

As an essential resource management problem in network virtualization, virtual network embedding (VNE) aims to allocate the finite resources of physical network to sequentially arr…

cs.IR20244 cited

A Unified Search and Recommendation Framework Based on Multi-Scenario Learning for Ranking in E-commerce

Jinhan Liu, Qiyu Chen, Junjie Xu +3

Search and recommendation (S&R) are the two most important scenarios in e-commerce. The majority of users typically interact with products in S&R scenarios, indicating the need and…

cs.CV202421 cited

3SHNet: Boosting Image-Sentence Retrieval via Visual Semantic-Spatial Self-Highlighting

Xuri Ge, Songpei Xu, Fuhai Chen +4

In this paper, we propose a novel visual Semantic-Spatial Self-Highlighting Network (termed 3SHNet) for high-precision, high-efficiency and high-generalization image-sentence retri…

cs.IR20244 cited

PPM : A Pre-trained Plug-in Model for Click-through Rate Prediction

Yuanbo Gao, Peng Lin, Dongyue Wang +4

Click-through rate (CTR) prediction is a core task in recommender systems. Existing methods (IDRec for short) rely on unique identities to represent distinct users and items that h…