14 citations · 57 across the 29 of their papers we have counts for
13 papers · 1 filter
OpticalRec: Unified Optical Vision-Language Representation for Multimodal Recommendation
Yueqi Wang, Zitian Guo, Yupeng Hou +9
Recent advances in vision-language modeling have substantially improved multimodal encoding, retrieval and reasoning. Yet for multimodal recommendation, encoding rich item vision-l…
Topology-Aware Tokenization for Generative Recommendation
Yaokun Liu, Yifan Liu, Zhenrui Yue +4
Generative recommendation reformulates sequential recommendation as an autoregressive generation task, yet a critical issue in this paradigm remains overlooked: topology distortion…
Retrieval Augmented Conversational Recommendation with Reinforcement Learning
Zhenrui Yue, Honglei Zhuang, Zhen Qin +4
Large language models (LLMs) exhibit enhanced capabilities in language understanding and generation. By utilizing their embedded knowledge, LLMs are increasingly used as conversati…
Boosting Data Utilization for Multilingual Dense Retrieval
Chao Huang, Fengran Mo, Yufeng Chen +5
Multilingual dense retrieval aims to retrieve relevant documents across different languages based on a unified retriever model. The challenge lies in aligning representations of di…
Learning Decomposed Contextual Token Representations from Pretrained and Collaborative Signals for Generative Recommendation
Yifan Liu, Yaokun Liu, Zelin Li +5
Recent advances in generative recommenders adopt a two-stage paradigm: items are first tokenized into semantic IDs using a pretrained tokenizer, and then large language models (LLM…
SPRINT: Scalable and Predictive Intent Refinement for LLM-Enhanced Session-based Recommendation
Gyuseok Lee, Wonbin Kweon, Zhenrui Yue +5
Large language models (LLMs) have enhanced conventional recommendation models via user profiling, which generates representative textual profiles from users' historical interaction…
Transferable Sequential Recommendation via Vector Quantized Meta Learning
Zhenrui Yue, Huimin Zeng, Yang Zhang +2
While sequential recommendation achieves significant progress on capturing user-item transition patterns, transferring such large-scale recommender systems remains challenging due…
Train Once, Deploy Anywhere: Matryoshka Representation Learning for Multimodal Recommendation
Yueqi Wang, Zhenrui Yue, Huimin Zeng +2
Despite recent advancements in language and vision modeling, integrating rich multimodal knowledge into recommender systems continues to pose significant challenges. This is primar…
Your Causal Self-Attentive Recommender Hosts a Lonely Neighborhood
Yueqi Wang, Zhankui He, Zhenrui Yue +2
In the context of sequential recommendation, a pivotal issue pertains to the comparative analysis between bi-directional/auto-encoding (AE) and uni-directional/auto-regressive (AR)…
Federated Recommendation via Hybrid Retrieval Augmented Generation
Huimin Zeng, Zhenrui Yue, Qian Jiang +1
Federated Recommendation (FR) emerges as a novel paradigm that enables privacy-preserving recommendations. However, traditional FR systems usually represent users/items with discre…
Linear Recurrent Units for Sequential Recommendation
Zhenrui Yue, Yueqi Wang, Zhankui He +3
State-of-the-art sequential recommendation relies heavily on self-attention-based recommender models. Yet such models are computationally expensive and often too slow for real-time…
LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking
Zhenrui Yue, Sara Rabhi, Gabriel de Souza Pereira Moreira +2
Recently, large language models (LLMs) have exhibited significant progress in language understanding and generation. By leveraging textual features, customized LLMs are also applie…
Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenders
Zhenrui Yue, Huimin Zeng, Ziyi Kou +2
While sequential recommender systems achieve significant improvements on capturing user dynamics, we argue that sequential recommenders are vulnerable against substitution-based pr…