arXiv AI
Jun 4
Rethinking Sales Lead Scoring with LLM-based Hierarchical Preference Ranking
arXiv:2606. 04387v1 Announce Type: cross Abstract: Sales lead conversion in high-stakes domains (e.
SalesLoop: Reinforcement Learning from Performance Feedback for Sales Lead Ranking
arXiv:2607. 20655v1 Announce Type: cross Abstract: Lead ranking in Customer Relationship Management (CRM) systems faces a persistent challenge: models achieving high offline accuracy often underperform in production.
Related stories
arXiv Machine Learning
Aug 11
A Control Function Framework for Mitigating Position Bias in Learning to Rank Systems
arXiv:2506. 06989v3 Announce Type: replace-cross Abstract: Learning-to-rank (LTR) systems commonly depend on implicit feedback, such as user clicks, because it is easy to collect and can serve as a valuable signal of user preferences.
arXiv AI
Jun 9
Adaptive Loss Balancing for Noise-Robust GRPO in Generative Recommendation
arXiv:2606. 08480v1 Announce Type: cross Abstract: Reinforcement learning (RL) presents a promising avenue for enhancing generative recommendation beyond supervised imitation, leveraging reward signals to guide policy improvement.
arXiv Computation and Language
Aug 27
Sell More, Play Less: Benchmarking LLM Realistic Selling Skill
The paper introduces SalesLLM, a bilingual (Chinese/English) benchmark for evaluating large language models (LLMs) in realistic sales dialogues. It comprises 30,074 scripted configurations and 1,805 curated multi‑turn scenarios from Financial Services and Consumer Goods, with controllable difficulty and personas. An automatic evaluation pipeline uses an LLM judge for sales‑process progress and fine‑tuned BERT classifiers for end‑of‑dialogue buying intent, while a user model, CustomerLM, is trained to improve simulation fidelity. SalesLLM scores correlate strongly with human ratings (Pearson r = 0.86) and reveal that top Chinese LLMs match junior‑to‑intermediate human salespeople but not experts, with cross‑lingual consistency remaining poor.
arXiv AI
Jun 26
AIGP: An LLM-Based Framework for Long-Term Value Alignment in E-Commerce Pricing
arXiv:2606. 26787v1 Announce Type: cross Abstract: Traditional dynamic pricing models in large-scale e-commerce suffer from limited interpretability, poor utilization of unstructured information, and misalignment with long-term business objectives such as cumulative Gross Merchandise Value (GMV), Return on Investment (ROI) and milestone achievement.
arXiv AI
Sep 16
Balancing Trial and Reorder: A Hybrid Sequential Transformer-GBDT Ranker for On-Demand Delivery
arXiv:2609.16407v1 Announce Type: cross Abstract: On a delivery platform, personalized store ranking greatly influences what users find and order. Unlike digital-only domains, candidate stores are lo...
arXiv Machine Learning
Aug 13
When Offline Evaluation Misleads: A Diagnostic Protocol for Reward and Policy Selection in Delayed-Feedback Contextual Bandits
arXiv:2608. 11560v1 Announce Type: new Abstract: Personalizing marketing messages with contextual multi-armed bandits (CMABs) drives real business value, yet the objective that ultimately matters - a downstream conversion - is observed only weeks later, too late to drive online learning.
Hugging Face Trending Papers
Aug 12
When Offline Evaluation Misleads: A Diagnostic Protocol for Reward and Policy Selection in Delayed-Feedback Contextual Bandits
Personalizing marketing messages with contextual multi-armed bandits (CMABs) drives real business value, yet the objective that ultimately matters - a downstream conversion - is observed only weeks later, too late to drive online learning. Teams therefore train the bandit on a fast proxy reward, and separately must judge whether a contextual bandit is worth its complexity over sending one best message.
arXiv Machine Learning
Sep 22
EvoRank: LLM-Guided Evolution of Multi-Objective Learning-to-Rank Pipelines
EvoRank is an open autonomous ranking engineer that uses an LLM-guided evolutionary loop to automatically design complete Learning-to-Rank pipelines—including features, models, losses, and ensembles—for multi-objective e-commerce search. On the Expedia ICDM 2013 dataset, EvoRank converged within 50 iterations on interpretable pipelines that outperform an Optuna-tuned LambdaMART on 60k held-out queries and rank in the top 6 % of the original competition. The authors also introduce a headroom gate that predicts whether the evolutionary loop will be worthwhile before any LLM computation, and they release the system, auditing tools, and a catalog of failure modes to help teams apply the method to their own ranking stacks.
arXiv Machine Learning
Sep 24
Evaluation Choices Decide the Forecasting Leaderboard: Evidence from a Production Marketplace Panel
The paper demonstrates that the outcome of a forecasting leaderboard is largely determined by the evaluator’s design choices rather than the models themselves. By fixing the data, horizon, and period, the authors varied three key evaluation decisions—unit of analysis, error pooling, and scoring metric—and showed that each can reverse or eliminate the apparent superiority of any forecasting method. The study also evaluates the practical impact of these choices on a deployed system, revealing that the selection rule captures a significant portion of the potential performance gain, and confirms the findings on an external public dataset.
arXiv Machine Learning
4d ago
Efficient Offline Learning of Ranking Policies via Top-$k$ Policy Decomposition
arXiv:2609.36740v1 Announce Type: new Abstract: Many recommender systems such as for e-commerce and news platforms aim to provide users with rankings they are likely to interact with. Off-Policy Lear...
arXiv AI
Sep 3
Discriminative World Models for Web Agents
The paper introduces Discriminative World Models for Web Agents, proposing a predicted-state matching objective that trains world models to produce representations that can distinguish the true resulting state from those of alternative actions. Using a branching dataset from WebArena Go-Browse, the authors demonstrate that this approach outperforms traditional supervised next-state prediction on a held‑out benchmark and improves action ranking on WebPRMBench. Additionally, employing the discriminative world model for test‑time action selection boosts end‑to‑end task success on WebArena‑Lite.