6 citations · 11 across the 13 of their papers we have counts for
10 papers · 1 filter
UserArena: Benchmarking Interactive User Simulation for Long-Horizon Shopping Agents
Jujia Zhao, Jiakai Tang, Jing Yu +6
Agentic recommendation pursues shopper goals through sequences of context-dependent decisions. Developing and evaluating such policies requires faithful recommendation world models…
RankSteer: Can Pointwise LLM Rankers Be Calibrated at the Representation Level?
Yumeng Wang, Catherine Chen, Suzan Verberne
Large language models (LLMs) are strong zero-shot pointwise rankers, but lag behind pairwise and listwise methods. Beyond missing comparative signals, we identify a \textit{calibra…
LANCER: LLM Reranking for Nugget Coverage
Jia-Huei Ju, François G. Landry, Eugene Yang +2
Unlike short-form retrieval-augmented generation (RAG), such as factoid question answering, long-form RAG requires retrieval to provide documents covering a wide range of relevant…
Differentiable Semantic ID for Generative Recommendation
Junchen Fu, Xuri Ge, Alexandros Karatzoglou +4
Generative recommendation provides a novel paradigm in which each item is represented by a discrete semantic ID (SID) learned from rich content. Most existing methods treat SIDs as…
Unifying Search and Recommendation in LLMs via Gradient Multi-Subspace Tuning
Jujia Zhao, Zihan Wang, Shuaiqun Pan +2
Search and recommendation (S&R) are core to online platforms, addressing explicit intent through queries and modeling implicit intent from behaviors, respectively. Their complement…
How role-play shapes relevance judgment in zero-shot LLM rankers
Yumeng Wang, Jirui Qi, Catherine Chen +2
Large Language Models (LLMs) have emerged as promising zero-shot rankers, but their performance is highly sensitive to prompt formulation. In particular, role-play prompts, where t…
Unifying Search and Recommendation with Dual-View Representation Learning in a Generative Paradigm
Jujia Zhao, Wenjie Wang, Chen Xu +3
Recommender systems and search engines serve as foundational elements of online platforms, with the former delivering information proactively and the latter enabling users to seek…
QUIDS: Query Intent Description for Exploratory Search via Dual Space Modeling
Yumeng Wang, Xiuying Chen, Suzan Verberne
In exploratory search, users often submit vague queries to investigate unfamiliar topics, but receive limited feedback about how the search engine understood their input. This lead…
What are the limits of cross-lingual dense passage retrieval for low-resource languages?
Jie Wu, Zhaochun Ren, Suzan Verberne
In this paper, we analyze the capabilities of the multi-lingual Dense Passage Retriever (mDPR) for extremely low-resource languages. In the Cross-lingual Open-Retrieval Answer Gene…
A Multi-Agent Conversational Recommender System
Jiabao Fang, Shen Gao, Pengjie Ren +3
Due to strong capabilities in conducting fluent, multi-turn conversations with users, Large Language Models (LLMs) have the potential to further improve the performance of Conversa…