arXiv AI
Jun 10
Atomic Intent Reasoning: Bringing LLM Semantics to Industrial Cross-Domain Recommendations
arXiv:2606. 10357v1 Announce Type: cross Abstract: Cross-domain recommendation is a core problem in content-to-e-commerce platforms.
From Explicit Elements to Implicit Intent: A Predefined Library for Auditable Behavioral Inference
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.
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arXiv Machine Learning
Sep 17
Behavior2Value: Benchmarking and Empowering LLMs for Consumer Value Measurement from E-commerce Behaviors
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 AI
Jul 9
Large Behavior Model: A Promptable Digital Twin of the Retail Customer
arXiv:2607. 06993v1 Announce Type: new Abstract: Customer behavior modeling underpins recommendation, marketing, and decision support, yet existing approaches either optimize predictive accuracy without explaining decisions or simulate users without grounding them in real behavioral data.
Hugging Face Trending Papers
Sep 16
Behavior2Value: Benchmarking and Empowering LLMs for Consumer Value Measurement from E-commerce Behaviors
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...
arXiv AI
Jul 7
MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation
arXiv:2603. 25126v2 Announce Type: replace-cross Abstract: Multi-Behavior Recommendation (MBR) leverages multiple user interaction types (e.
arXiv AI
Aug 3
DRIP-R: A Benchmark for Decision-Making and Reasoning Under Real-World Policy Ambiguity in the Retail Domain
arXiv:2605. 07699v2 Announce Type: replace-cross Abstract: LLM-based agents are increasingly deployed for routine but consequential tasks in real-world domains, where their behavior is governed by inherently ambiguous domain policies that admit multiple valid interpretations.
arXiv Machine Learning
Aug 27
DCEO: Direct Causal Effect Optimization for Long-Term User Value Modeling in E-commerce Search
The paper introduces DCEO, a data‑driven framework that learns item‑level proxy scores directly aligned with long‑term user objectives in e‑commerce search. It aggregates these scores into a user‑level metric, measures alignment via relative causal effect, and uses an actor‑critic model to generate context‑dependent fusion weights for multiple objectives. Offline experiments and a 41‑day online A/B test show DCEO improves GMV by 0.36% over traditional proxies.
arXiv AI
Jun 16
RetailBench: Benchmarking long horizon reasoning and coherent decision making of LLM agents in realistic retail environments
arXiv:2606. 15862v1 Announce Type: new Abstract: Large language model (LLM) agents have made rapid progress on short-horizon, well-scoped tasks, yet their ability to sustain coherent decisions in dynamic long-horizon environments remains uncertain.
arXiv Computation and Language
Sep 1
LLP: LLM-Based Product Pricing in E-commerce
The paper introduces LLP, a Large Language Model–based generative framework for pricing second‑hand products on consumer‑to‑consumer platforms. LLP retrieves similar items to capture market dynamics, then uses LLMs to generate price suggestions, refined through supervised fine‑tuning and group relative policy optimization. A confidence‑based filter rejects unreliable predictions, and experiments show LLP outperforms prior methods, achieving higher static adoption rates when deployed on Xianyu.
Hugging Face Trending Papers
Jul 28
SPARC Segmentation to Prediction via Affine Regression and Counterfactuals
Transaction propensity prediction in B2B e commerce presents unique challenges distinct from B2C contexts, primarily due to the heterogeneous procurement behaviors of organizational entities, which violate SMOTE's implicit assumption of within class feature homogeneity. Specifically, B2B buyers exhibit multi modal procurement cycles that render linear interpolation between minority class samples structurally invalid, producing synthetic data that does not represent real purchasing behavior.
arXiv AI
Sep 1
The Differential Reasoning Router: Operationalizing Cost-Aware LLM Annotation in E-commerce
arXiv:2608.30224v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used to annotate structured product data in e-commerce, but early deployment often begins as a cold-sta...
Hugging Face Trending Papers
Aug 4
SeqLLM: Augmenting LLMs with Behavioral-Sequence Modeling for High-Stakes Decisions at WeChat Pay
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.