For e-commerce retailers, high-quality product catalogs are vital to customer experience. Yet, despite lots of data cleaning efforts, catalog quality, especially in large catalogs, remains suboptimal. This paper shows how to use unstructured brand knowledge base data as a reference and a large language model agent to automatically enhance an e-commerce retailer's catalog quality. Unlike prior methods that usually repair and match product entries separately, our method does both concurrently. Our evaluation results show its effectiveness.

While both Internet Service Providers (ISPs) and third-party Security Service Providers (SSPs) offer Distributed Denial-of-Service (DDoS) mitigation services through cloud-based scrubbing centers, it is often beneficial for ISPs to outsource part of the traffic scrubbing to SSPs to achieve less economic cost and better network performance. To explore this potential, we design an online auction mechanism, featured by the challenge of the switching cost of using different winning bids over time. Formulating the social cost minimization as a nonconvex integer program, we firstly relax it and design an online algorithm that breaks it into a series of modified single-shot problems and solves each of them in polynomial time, without requiring knowledge of future inputs; then, we design a randomized rounding algorithm to convert the fractional decisions into integers without violating any constraints; and finally, we design the payment for each bid based on its winning probability. We rigorously prove that our mechanism achieves a parameterized-constant competitive ratio for the long-term social cost, with truthfulness and individual rationality in expectation. We also exhibit its superior practical performance via evaluations driven by real-world data traces.

The strategic management of distributed resources of mobile edge computing networks often requires managing different system components over different timescales. In this paper, we formulate a nonlinear mixed-integer program to capture the online optimization of the edge network’s long-term cost, where we distribute workload more frequently on the fast timescale and provision resources less frequently on the slow timescale. We design a novel online learning framework consisting of three algorithms to make fast-timescale and slow-timescale fractional decisions, respectively, and round such decisions into integers. Our algorithms run in polynomial time in an online manner, jointly solving the original NP-hard problem that can contain arbitrary and unpredictable inputs. Via rigorous formal analysis, we prove a parameterized-constant competitive ratio as the performance guarantee for our approach. We conduct extensive evaluations with real-world data and confirm our approach’s superiority over existing practices and state-of-the-arts.