7 citations · 14 across the 9 of their papers we have counts for
10 papers
SA-CAISR: Stage-Adaptive and Conflict-Aware Incremental Sequential Recommendation
Xiaomeng Song, Xinru Wang, Hanbing Wang +4
Sequential recommendation (SR) aims to predict a user's next action by learning from their historical interaction sequences. In real-world applications, these models require period…
DeepRec: Towards a Deep Dive Into the Item Space with Large Language Model Based Recommendation
Bowen Zheng, Xiaolei Wang, Enze Liu +5
Recently, large language models (LLMs) have been introduced into recommender systems (RSs), either to enhance traditional recommendation models (TRMs) or serve as recommendation ba…
Universal Item Tokenization for Transferable Generative Recommendation
Bowen Zheng, Hongyu Lu, Yu Chen +2
Recently, generative recommendation has emerged as a promising paradigm, attracting significant research attention. The basic framework involves an item tokenizer, which represents…
Slow Thinking for Sequential Recommendation
Junjie Zhang, Beichen Zhang, Wenqi Sun +4
To develop effective sequential recommender systems, numerous methods have been proposed to model historical user behaviors. Despite the effectiveness, these methods share the same…
Enhancing Graph Contrastive Learning with Reliable and Informative Augmentation for Recommendation
Bowen Zheng, Junjie Zhang, Hongyu Lu +4
Graph neural network(GNN) has been a powerful approach in collaborative filtering(CF) due to its ability to model high-order user-item relationships. Recently, to alleviate the dat…
Cross Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction
Ruijie Hou, Zhaoyang Yang, Yu Ming +5
Deep neural networks (DNNs) that incorporated lifelong sequential modeling (LSM) have brought great success to recommendation systems in various social media platforms. While conti…
Scaling Law of Large Sequential Recommendation Models
Gaowei Zhang, Yupeng Hou, Hongyu Lu +3
Scaling of neural networks has recently shown great potential to improve the model capacity in various fields. Specifically, model performance has a power-law relationship with mod…
Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation
Bowen Zheng, Yupeng Hou, Hongyu Lu +4
Recently, large language models (LLMs) have shown great potential in recommender systems, either improving existing recommendation models or serving as the backbone. However, there…
Streaming CTR Prediction: Rethinking Recommendation Task for Real-World Streaming Data
Qi-Wei Wang, Hongyu Lu, Yu Chen +4
The Click-Through Rate (CTR) prediction task is critical in industrial recommender systems, where models are usually deployed on dynamic streaming data in practical applications. S…
Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising
Dongbo Xi, Zhen Chen, Peng Yan +4
In most real-world large-scale online applications (e.g., e-commerce or finance), customer acquisition is usually a multi-step conversion process of audiences. For example, an impr…