5 papers
UniSGR-V2: Stepwise Target-Side Feature Anticipation and Crossing for Generative Recommendation
Jun Yang, Jiasi Chen, Jiawei Sun +4
Semantic-ID-based generative recommendation unifies retrieval and ranking through autoregressive decoding but differs structurally from discriminative models, which can directly ac…
UniSGR: Unified Framework for Semantic ID Generation and Ranking
Jiawei Sun, Jun Yang, Ziyue Guo +4
Recommendation systems play a pivotal role in modern e-commerce platforms. While generative retrieval has emerged as a promising paradigm for alleviating the limitations of multi-s…
Querit-Reranker: Training Compact Multilingual Rerankers via Efficient Label-Free Distribution Adaptation
Yunfei Zhong, Jun Yang, Wei Huang +7
A deployable multilingual reranker must not only generalize across languages, domains, and ranking tasks, but also remain efficient to serve as a second-stage reranker in practical…
Reason to Retrieve: Enhancing Query Understanding through Decomposition and Interpretation
Yunfei Zhong, Jun Yang, Yixing Fan +4
Query understanding (QU) aims to accurately infer user intent to improve document retrieval. It plays a vital role in modern search engines. While large language models (LLMs) have…
Towards AI Search Paradigm
Yuchen Li, Hengyi Cai, Rui Kong +20
In this paper, we introduce the AI Search Paradigm, a comprehensive blueprint for next-generation search systems capable of emulating human information processing and decision-maki…