5 citations · 9 across the 13 of their papers we have counts for
6 papers · 1 filter
What Makes a Good Semantic ID for Generative Recommendation? A Reproducibility Study
Yufei Chen, Junchen Fu, Jujia Zhao +2
Generative recommendation has emerged as an active research direction, where items are commonly represented by semantic IDs (SIDs): discrete codes generated token by token. Despite…
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…
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…
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…
Generative Retrieval with Few-shot Indexing
Arian Askari, Chuan Meng, Mohammad Aliannejadi +3
Existing generative retrieval (GR) methods rely on training-based indexing, which fine-tunes a model to memorise associations between queries and the document identifiers (docids)…