1 citations · 2 across the 11 of their papers we have counts for
12 papers
GenRecEdit: Adapting Model Editing for Generative Recommendation with Cold-Start Items
Chenglei Shen, Teng Shi, Weijie Yu +2
Generative recommendation (GR) has shown strong potential for sequential recommendation in an end-to-end generation paradigm. However, existing GR models suffer from severe cold-st…
LLaDA-Rec: Discrete Diffusion for Parallel Semantic ID Generation in Generative Recommendation
Teng Shi, Chenglei Shen, Weijie Yu +6
Generative recommendation represents each item as a semantic ID, i.e., a sequence of discrete tokens, and generates the next item through autoregressive decoding. While effective,…
Merge and Guide: Unifying Model Merging and Guided Decoding for Controllable Multi-Objective Generation
Guofu Xie, Chen Zhang, Xiao Zhang +3
Adapting to diverse user needs at test time is a key challenge in controllable multi-objective generation. Existing methods are insufficient: merging-based approaches provide indir…
Balancing Stylization and Truth via Disentangled Representation Steering
Chenglei Shen, Zhongxiang Sun, Teng Shi +2
Generating stylized large language model (LLM) responses via representation editing is a promising way for fine-grained output control. However, there exists an inherent trade-off:…
Bridging Search and Recommendation through Latent Cross Reasoning
Teng Shi, Weicong Qin, Weijie Yu +4
Search and recommendation (S&R) are fundamental components of modern online platforms, yet effectively leveraging search behaviors to improve recommendation remains a challenging p…
Benefit from Rich: Tackling Search Interaction Sparsity in Search Enhanced Recommendation
Teng Shi, Weijie Yu, Xiao Zhang +3
In modern online platforms, search and recommendation (S&R) often coexist, offering opportunities for performance improvement through search-enhanced approaches. Existing studies s…
Test-Time Alignment for Tracking User Interest Shifts in Sequential Recommendation
Changshuo Zhang, Xiao Zhang, Teng Shi +2
Sequential recommendation is essential in modern recommender systems, aiming to predict the next item a user may interact with based on their historical behaviors. However, real-wo…
Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation
Jiakai Tang, Sunhao Dai, Teng Shi +5
Sequential Recommendation (SeqRec) aims to predict the next item by capturing sequential patterns from users' historical interactions, playing a crucial role in many real-world rec…
Modeling Domain and Feedback Transitions for Cross-Domain Sequential Recommendation
Changshuo Zhang, Teng Shi, Xiao Zhang +4
Nowadays, many recommender systems encompass various domains to cater to users' diverse needs, leading to user behaviors transitioning across different domains. In fact, user behav…
A Survey of Controllable Learning: Methods and Applications in Information Retrieval
Chenglei Shen, Xiao Zhang, Teng Shi +3
Controllability has become a crucial aspect of trustworthy machine learning, enabling learners to meet predefined targets and adapt dynamically at test time without requiring retra…
Do Not Wait: Learning Re-Ranking Model Without User Feedback At Serving Time in E-Commerce
Yuan Wang, Zhiyu Li, Changshuo Zhang +4
Recommender systems have been widely used in e-commerce, and re-ranking models are playing an increasingly significant role in the domain, which leverages the inter-item influence…
QAGCF: Graph Collaborative Filtering for Q&A Recommendation
Changshuo Zhang, Teng Shi, Xiao Zhang +5
Question and answer (Q&A) platforms usually recommend question-answer pairs to meet users' knowledge acquisition needs, unlike traditional recommendations that recommend only one i…