Capacity-Constrained Influence Maximization: Algorithms and Applications

The paper introduces Capacity‑Constrained Influence Maximization (CIM), a framework that selects up to k neighbors per active user to maximize spread under node capacity limits, proposes MG‑Greedy and RR‑Greedy algorithms with ≥½ approximation, and demonstrates the near‑linear RR‑OPIM+ method’s superior accuracy and speed on large social networks and a Tencent game recommendation system.

Big DataCapacity ConstraintKDD 2023

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Capacity-Constrained Influence Maximization: Algorithms and Applications

Meituan Technology Team

Aug 10, 2023 · Artificial Intelligence

Selected Meituan Technical Papers from KDD 2023: Summaries of Seven Research Works

The article showcases seven Meituan research papers accepted at KDD 2023—spanning feed‑stream, cross‑domain, takeaway, bonus allocation, contour‑based segmentation, living‑needs prediction, and multilingual recommendation—detailing their novel methods, real‑world deployments, and concluding with an invitation for academic collaboration.

Artificial IntelligenceKDD 2023Meituan

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Selected Meituan Technical Papers from KDD 2023: Summaries of Seven Research Works