This work introduces SHOPPER, which addresses bottlenecks in large-scale e-commerce recommendation systems through Semantic history, order-aware local intent modeling, and serving-aware factorization, enabling deep target-aware summarization.
Integrating recall and pre-ranking in e-commerce search requires candidate generation to account for relevance, personalization, and business value before final ranking. To this end, we present VARG, a generative retrieval system for Tmall App search that directly admits generated item candidates to the existing final...
Xiao-Peng Chu, Jian-Bo Zhu, Ming-Min Jin et al.· 0 citations
E-commerce platforms increasingly display clickable query suggestions alongside items in the user feed, enabling users to refine or expand their intent without manually reformulating queries. Existing approaches either mine suggestions from historical logs -- limited to past behavior and blind to long-tail, personalize...
Shu-Wei Yuan, Mingyu Ding, Lu-Xin Liu et al.· 0 citations
Multi-merchant e-commerce catalogs contain equivalent and related products under different merchant-scoped identifiers, fragmenting behavioral evidence across merchants. Expert-defined taxonomies, meanwhile, are often too coarse for fine-grained discovery. We investigate whether a single hierarchical Semantic ID (\sid{...
Steven Xu, Sanjyot Thete, Saathvik Dirisala et al.· 0 citations
Traditional e-commerce search platforms rely heavily on inverted indices and token-level lexical matching algorithms (e.g., BM25 and TF-IDF), which frequently fail on conversational, intent-driven, or paraphrased user queries -- the classic vocabulary mismatch problem. We formulate conversational product recommendation...
While Large Language Models (LLMs) have significantly advanced reranking in recommendation, effectively leveraging item-side information remains challenging. Real-world items are described by vast, heterogeneous, and unstructured metadata, where decision-relevant signals are often implicit, noisy, or buried in long des...
Dojun Hwang, Seunghan Lee, Cheonyoung Park et al.· 0 citations
Trade-up recommendation identifies higher-quality alternatives that preserve a customer's purchase intent while offering upgraded benefits. Large language models (LLMs) can reason about such distinctions, but applying them directly to hundreds of millions of product pairs is operationally impractical. We introduce a tw...
Si-Liang Liu, Mohammadhasan Ghasemi, Sapan Patel et al.· 0 citations
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