301 citations · 718 across the 46 of their papers we have counts for
11 papers · 1 filter
OneSearch: A Preliminary Exploration of the Unified End-to-End Generative Framework for E-commerce Search
Ben Chen, Xian Guo, Siyuan Wang +25
Traditional e-commerce search systems employ multi-stage cascading architectures (MCA) that progressively filter items through recall, pre-ranking, and ranking stages. While effect…
Towards Context-aware Reasoning-enhanced Generative Searching in E-commerce
Zhiding Liu, Ben Chen, Mingyue Cheng +6
Search-based recommendation is one of the most critical application scenarios in e-commerce platforms. Users' complex search contexts--such as spatiotemporal factors, historical in…
OneVision: An End-to-End Generative Framework for Multi-view E-commerce Vision Search
Zexin Zheng, Huangyu Dai, Lingtao Mao +8
Traditional vision search, similar to search and recommendation systems, follows the multi-stage cascading architecture (MCA) paradigm to balance efficiency and conversion. Specifi…
Fading to Grow: Growing Preference Ratios via Preference Fading Discrete Diffusion for Recommendation
Guoqing Hu, An Zhang. Shuchang Liu, Wenyu Mao +7
Recommenders aim to rank items from a discrete item corpus in line with user interests, yet suffer from extremely sparse user preference data. Recent advances in diffusion models h…
An End-to-End Multi-objective Ensemble Ranking Framework for Video Recommendation
Tiantian He, Minzhi Xie, Runtong Li +6
We propose a novel End-to-end Multi-objective Ensemble Ranking framework (EMER) for the multi-objective ensemble ranking module, which is the most critical component of the short v…
GoalRank: Group-Relative Optimization for a Large Ranking Model
Kaike Zhang, Xiaobei Wang, Shuchang Liu +7
Mainstream ranking approaches typically follow a Generator-Evaluator two-stage paradigm, where a generator produces candidate lists and an evaluator selects the best one. Recent wo…
Denoising Neural Reranker for Recommender Systems
Wenyu Mao, Shuchang Liu, Hailan Yang +9
For multi-stage recommenders in industry, a user request would first trigger a simple and efficient retriever module that selects and ranks a list of relevant items, then the recom…
Representation Quantization for Collaborative Filtering Augmentation
Yunze Luo, Yinjie Jiang, Gaode Chen +9
As the core algorithm in recommendation systems, collaborative filtering (CF) algorithms inevitably face the problem of data sparsity. Since CF captures similar users and items for…
Learning Time-Aware Causal Representation for Model Generalization in Evolving Domains
Zhuo He, Shuang Li, Wenze Song +4
Endowing deep models with the ability to generalize in dynamic scenarios is of vital significance for real-world deployment, given the continuous and complex changes in data distri…
Who You Are Matters: Bridging Topics and Social Roles via LLM-Enhanced Logical Recommendation
Qing Yu, Xiaobei Wang, Shuchang Liu +14
Recommender systems filter contents/items valuable to users by inferring preferences from user features and historical behaviors. Mainstream approaches follow the learning-to-rank…
Comprehensive List Generation for Multi-Generator Reranking
Hailan Yang, Zhenyu Qi, Shuchang Liu +6
Reranking models solve the final recommendation lists that best fulfill users' demands. While existing solutions focus on finding parametric models that approximate optimal policie…