557 citations · 652 across the 10 of their papers we have counts for
10 papers
LaRec: Unleashing LLM-based Latent Reasoning for Generative Recommendation
Yu Xia, Zihan Lin, Wei Yang +4
Large Language Models (LLMs) have shown great promise in recommendation due to superior reasoning abilities. However, existing methods mainly rely on explicit Chain-of-Thought (CoT…
TimeMM: Time-as-Operator Spectral Filtering for Dynamic Multimodal Recommendation
Wei Yang, Rui Zhong, Zihan Lin +4
Multimodal recommendation improves user modeling by integrating collaborative signals with heterogeneous item content. In real applications, user interests evolve over time and exh…
Action-Aware Generative Sequence Modeling for Short Video Recommendation
Wenhao Li, Zihan Lin, Zhengxiao Guo +7
With the rapid development of the Internet, users have increasingly higher expectations for the recommendation accuracy of online content consumption platforms. However, short vide…
Large Language Models are Zero-Shot Rankers for Recommender Systems
Yupeng Hou, Junjie Zhang, Zihan Lin +4
Recently, large language models (LLMs) (e.g., GPT-4) have demonstrated impressive general-purpose task-solving abilities, including the potential to approach recommendation tasks.…
Robust Failure Diagnosis of Microservice System through Multimodal Data
Shenglin Zhang, Pengxiang Jin, Zihan Lin +10
Automatic failure diagnosis is crucial for large microservice systems. Currently, most failure diagnosis methods rely solely on single-modal data (i.e., using either metrics, logs,…
Modeling Adaptive Fine-grained Task Relatedness for Joint CTR-CVR Estimation
Zihan Lin, Xuanhua Yang, Xiaoyu Peng +4
In modern advertising and recommender systems, multi-task learning (MTL) paradigm has been widely employed to jointly predict diverse user feedbacks (e.g. click and purchase). Whil…
RecBole 2.0: Towards a More Up-to-Date Recommendation Library
Wayne Xin Zhao, Yupeng Hou, Xingyu Pan +16
In order to support the study of recent advances in recommender systems, this paper presents an extended recommendation library consisting of eight packages for up-to-date topics a…
Feature-aware Diversified Re-ranking with Disentangled Representations for Relevant Recommendation
Zihan Lin, Hui Wang, Jingshu Mao +4
Relevant recommendation is a special recommendation scenario which provides relevant items when users express interests on one target item (e.g., click, like and purchase). Besides…
Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning
Zihan Lin, Changxin Tian, Yupeng Hou +1
Recently, graph collaborative filtering methods have been proposed as an effective recommendation approach, which can capture users' preference over items by modeling the user-item…
RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms
Wayne Xin Zhao, Shanlei Mu, Yupeng Hou +16
In recent years, there are a large number of recommendation algorithms proposed in the literature, from traditional collaborative filtering to deep learning algorithms. However, th…