arXiv AI By Tripti Shukla, Shwetha Somasundaram, Akshat Gupta, Rohit Garg, Paul Duff, Ian Anderson

FASHION-RQ: Mining Hierarchical Semantic Structure for Automated Evaluation of Fashion Retrieval

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv AI
Sep 1

Preference Shapes Relevance: Cross-component Hierarchical Semantic Alignment for Personalized Generative Retrieval

The paper introduces CHAP, a personalized generative retrieval framework that aligns query semantics with item representations through a hierarchical semantic alignment module and models user behavior using both discrete Semantic IDs and continuous representations. It also proposes a Residual Cascading Generation mechanism to reduce inference latency by limiting the Transformer decoder to a single pass. Experiments on multiple datasets and online A/B tests show that CHAP outperforms existing methods, demonstrating its practical value.

By Gaoming Zhang, Angqing Jiang, Jianchun Song, Kena Qi, Dayao Chen, Wei Lin, Defu Lian
arXiv AI
Oct 12

Beyond Semantic IDs: Finite-Beam Reachability and Continuous-Query Generation in Generative Recommendation

The paper introduces two continuous-query paradigms—CQG‑Single and CQG‑AR—to replace the traditional finite-beam, discrete Semantic ID (SID) decoding used in generative recommendation systems. By removing the SID vocabulary and beam expansion, these methods allow every encoded item to be directly scored via dense or approximate-nearest-neighbor retrieval, thereby eliminating the reachability constraints of finite-beam decoding. Experiments show that CQG‑Single outperforms a TIGER-style baseline across all runs while using fewer parameters, and CQG‑AR performs comparably or better on several datasets, suggesting that decoupling output representation, autoregressive modeling, and retrieval budget can improve generative recommendation evaluation.

By Yu Sun, Renkai Xiang, Zuodong Xiang, Junwei Zhou, Yike Zhang, Jianxin Zhang, Zhihao Zhu, Xiaoyu Cao, Hailu Xu
arXiv Computer Vision
Sep 22

Graded-Relevance Composed Multimodal Retrieval for E-commerce Visual Search at Scale

The paper introduces GradCIR, a method for training composed image retrieval (CIR) systems on graded relevance rather than binary relevance. It uses a vision‑language model to generate queries and 4‑level relevance labels, an iterative feedback loop to mine hard negatives, and a hierarchy‑aware angular objective to directly optimize graded labels. Experiments on a Walmart catalog and FashionIQ show significant NDCG improvements and the system is deployed in Walmart’s live visual‑search traffic.

By Anubhav Gupta, Hrushikesh Mohapatra, Prijith Chandra, Asish Mohapatra, Anuj Garg, Arvind Maan, Sudip Datta, Venkat Bulusu, Sitesh Kumar Jalan