1 citations · 3 across the 8 of their papers we have counts for
7 papers · 1 filter
Collaborative Memory Augmentation for Generative Recommendation
Enze Liu, Zhen Tian, Wayne Xin Zhao
Generative Recommendation (GR) has exhibited great potential by modeling item transitions as a sequence-to-sequence task. Despite the success of GR, existing frameworks primarily f…
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…
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…
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…
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…