9 citations · 18 across the 12 of their papers we have counts for
13 papers
ClawRec: A Claw-Native Recommender System
Chenghao Wu, Kesha Ou, Xiaolei Wang +8
Recommender systems have become integral to navigating the modern digital ecosystem. Yet most deployed systems remain confined within single-platform boundaries, observing localize…
Guess Where You Go: Generative Next Point-of-Interest Recommendation in Amap
Penglong Zhai, Bowen Zheng, Jie Li +8
Generative retrieval enables recommender systems to retrieve items by generating compact item identifiers, but scaling it to industrial scenarios remains challenging due to redunda…
Generative Spatiotemporal Intent Sequence Recommendation via Implicit Reasoning in Amap
Sicong Wang, Ruiting Dong, Yue Liu +7
Real-world user behavior rarely consists of isolated actions; instead, it often forms intent flows governed by spatiotemporal dependencies. To provide integrated service recommenda…
LARES: Latent Reasoning for Sequential Recommendation
Enze Liu, Bowen Zheng, Xiaolei Wang +4
Sequential recommender systems have become increasingly important in real-world applications that model user behavior sequences to predict their preferences. However, existing sequ…
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…
Pre-training Generative Recommender with Multi-Identifier Item Tokenization
Bowen Zheng, Enze Liu, Zhongfu Chen +4
Generative recommendation autoregressively generates item identifiers to recommend potential items. Existing methods typically adopt a one-to-one mapping strategy, where each item…
Bridging Textual-Collaborative Gap through Semantic Codes for Sequential Recommendation
Enze Liu, Bowen Zheng, Wayne Xin Zhao +1
In recent years, substantial research efforts have been devoted to enhancing sequential recommender systems by integrating abundant side information with ID-based collaborative inf…
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…
Generative Recommender with End-to-End Learnable Item Tokenization
Enze Liu, Bowen Zheng, Cheng Ling +3
Generative recommendation systems have gained increasing attention as an innovative approach that directly generates item identifiers for recommendation tasks. Despite their potent…
LLMBox: A Comprehensive Library for Large Language Models
Tianyi Tang, Yiwen Hu, Bingqian Li +22
To facilitate the research on large language models (LLMs), this paper presents a comprehensive and unified library, LLMBox, to ease the development, use, and evaluation of LLMs. T…
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…
A Locality-based Neural Solver for Optical Motion Capture
Xiaoyu Pan, Bowen Zheng, Xinwei Jiang +8
We present a novel locality-based learning method for cleaning and solving optical motion capture data. Given noisy marker data, we propose a new heterogeneous graph neural network…