The paper presents a three‑phase pipeline that shifts semantic user profiling from per‑user to per‑transaction‑pattern inference, dramatically reducing LLM inference costs. The Resolve phase maps item names to abstracts, Profile generates categorical labels, free‑text attributes, and prevalence estimates per pattern, and Tag clusters these attributes into a searchable database. Evaluated on a public e‑commerce corpus, the pattern‑based approach matches the performance of a per‑user LLM while enabling deployment at a major Japanese bank with tens of millions of users and a three‑order‑of‑magnitude reduction in inference targets.
arXiv:2608.30023v1 Announce Type: cross Abstract: Generative engines such as ChatGPT, Gemini, and Perplexity answer buyer questions directly and name a shortlist of brands inside the answer. Studying...
Merchant risk control at large payment platforms screens tens of millions of merchants daily, where false positives harm legitimate merchants and false negatives leave harmful activity undetected. The hardest cases require jointly understanding a merchant's textual profile and long behavioral sequence.
The paper describes a new approach for two‑sided service marketplaces that replaces fixed request forms with AI‑native probabilistic matching using large language models. It introduces an autoresearch loop that generates a provider‑side preference taxonomy for each occupation, iteratively refining candidate tag sets through a six‑rubric LLM judge and a seven‑critic panel. The system also maps legacy form questions back to the new taxonomy, enabling coverage assessment and human quality assurance.
arXiv:2606. 11207v1 Announce Type: new Abstract: We present SemantiClean, a modular framework for extracting structured semantic signals from e-commerce session data and driving pluggable inference targets including purchase intent, customer segmentation, and product affinity through a shared element library.
Human values are deep motivational orientations that shape human behaviors. In e-commerce, they reveal the stable drivers behind users' purchase decisions. Compared with short-term interests, consumer...
TRACE is a new framework that uses agentic Large Language Models to automatically enrich e-commerce product catalogs with missing or buried attributes. It employs a ScoutAgent to gather multimodal evidence from merchant catalogs, syndicated feeds, and web search, and a JudgeAgent to verify and publish the proposed attribute values. In offline evaluation, TRACE achieved 98.2% accuracy with 74.7% coverage, and in production it increased enrichment coverage by 90.4% and boosted checkout conversion by 0.48%.
The paper introduces the Behavior-to-Value (B2V) task, which seeks to identify consumer values from e-commerce behavioral trajectories. It presents the E-commerce Consumption Value Taxonomy (ECVT) and the B2V-Bench dataset, derived from anonymized Taobao logs and covering 25 purchase behaviors with associated value orientations. A new model, B2V-Verifier, is proposed to improve value measurement accuracy, achieving a 34% boost in multi-label classification over strong LLM baselines.
arXiv:2606. 05257v1 Announce Type: new Abstract: Foundation models are increasingly trained on sequences of user actions in recommendation, payments, fraud, and commerce, but these models still lack the kind of compute calibration that scaling laws provide for language models.
arXiv:2607. 26545v1 Announce Type: cross Abstract: We propose an index for predicting the U.
arXiv:2606.26403v2 Announce Type: replace Abstract: Foundation-model research increasingly needs data about people: user state, personal histories, relationships, contact-like fields, documents, and...
TxnQA is a new analytical QA benchmark that tests reasoning and evidence grounding over transaction event sequences. It contains 27 task families with 81 templates and a 5‑scenario fraud‑risk reasoning suite built on synthetic, privacy‑mitigated transaction data. The benchmark evaluates models using format‑following rate, conditional content accuracy, and end‑to‑end correctness, revealing distinct bottlenecks in schema compliance, semantic calculation, and evidence attribution.
arXiv:2610.34790v1 Announce Type: new Abstract: User memory helps personalized systems build on their understanding of users over time. In e-commerce, users convey their preferences and needs through...