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Hugging Face Trending Papers

Budget-First Tariff Recommendation (BFTR): A Complete Algorithmic Framework for Telecom Plan Recommendation without Overcharging

The paper introduces BFTR, a Budget‑First Tariff Recommendation framework that offers eight algorithmic strategies, including two novel hybrid approaches (Recursive Hybrid and Knapsack‑First Hybrid). It mathematically guarantees no overcharging by aligning final prices with catalog reference prices and proves that a suitable offer exists for any positive budget. Experiments on 974 Nigerian MTN customers show all strategies achieve zero surcharge, with Recursive Hybrid and Piecewise delivering optimal budget usage and volume, respectively, while maintaining sub‑10 ms execution times.

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arXiv AI
Aug 20

Budget-First Tariff Recommendation (BFTR): A Complete Algorithmic Framework for Telecom Plan Recommendation without Overcharging

The paper introduces Budget-First Tariff Recommendation (BFTR), an algorithmic framework that offers telecom plans without overcharging by aligning final prices with catalog reference prices. BFTR incorporates eight Budget-First strategies, including two novel hybrid approaches—Recursive Hybrid and Knapsack-First Hybrid— and mathematically proves that a suitable offer exists for any positive budget with zero surcharge for non‑interpolated strategies. Experiments on a Nigerian MTN‑inspired dataset show that all strategies achieve zero overcharging, with Recursive Hybrid delivering optimal customer utility and Piecewise maximizing volume, while maintaining sub‑10 ms execution times.

By Ghislain Dorian Tchuente Mondjo
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arXiv Machine Learning
Jul 22

JD-BP: A Joint-Decision Generative Framework for Auto-Bidding and Pricing

arXiv:2604. 05845v2 Announce Type: replace-cross Abstract: Auto-bidding services optimize real-time bidding strategies for advertisers under key performance indicator (KPI) constraints such as target return on investment and budget.

By Linghui Meng, Chun Gan, Shengsheng Niu, Chengcheng Zhang, Chenchen Li, Chuan Yang, Yi Mao, Xin Zhu, Jie He, Zhangang Lin, Ching Law
reinforcement-learningbenchmarkssafety
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arXiv AI
Aug 26

The Shadow Price of Intelligence: Quality Degradation in LLM Inference as a Supply Chain Problem

The paper argues that large language model (LLM) providers, constrained by compute, often degrade service during congestion by routing queries to smaller models, cutting reasoning effort, or truncating context. It shows that this practice misrepresents costs because degraded answers can fail, leading to retries that inflate traffic or churn that erodes lifetime value. By modeling inference allocation with newsvendor, retry, and queueing frameworks, the authors derive a ‘shadow price of intelligence’ that quantifies the marginal value of each query, revealing that throttling under congestion acts as a demand lever rather than a cost lever.

By Elioth Sanabria
llms
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arXiv Machine Learning
Aug 31

Robust Assortment Optimization from Observational Data

The paper introduces a robust framework for assortment optimization that addresses distributional shifts in customer choice behavior. It demonstrates computational tractability when the nominal choice model is known and develops statistically optimal algorithms for the data‑driven setting, providing matching upper and lower bounds on sample complexity. The authors identify "robust item‑wise coverage" as the minimal data requirement for efficient robust learning, bridging robustness and statistical efficiency in assortment planning.

By Miao Lu, Yuxuan Han, Han Zhong, Zhengyuan Zhou, Jose Blanchet
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