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The Ali Mama External Investment & Brand Algorithm Series compiles recent KDD, NAACL, and SIGIR research on ad allocation, pacing, traffic estimation, and creative optimization, offering a comprehensive overview of cutting‑edge techniques that enhance advertising efficiency and brand performance across digital platforms.
Alibaba’s Mama technical team secured four paper acceptances at the prestigious KDD 2024 conference in Barcelona, presenting advances such as a diffusion‑based generative bidding model, truthful combinatorial bandit mechanisms for two‑stage ad auctions, bi‑objective contract allocation for guaranteed delivery advertising, and a fast local‑search algorithm for complex contract constraints.
At KDD 2021, Alibaba Mama presented six papers that introduced a unified constrained‑bidding solution, a deep‑learnable auction mechanism, real‑negative training for delayed‑feedback CVR, a contextual‑bandit advertising strategy recommender, a multi‑agent cooperative bidding game, and an uncertainty‑aware exploration model, all of which have been deployed to boost platform revenue and advertiser performance.
The 3rd International Workshop on Deep Learning Practice for High-Dimensional Sparse Data (DLP‑KDD 2021) invites submissions on deep‑learning systems, data representation, and user modeling for large‑scale sparse data, with a submission deadline of May 10 2021 and results announced on June 10 2021.
The paper proposes a two‑stage framework that first fits a differential‑equation‑based exposure‑click (P2C) model for each new video and then uses a genetic‑algorithm multi‑objective optimization to allocate scarce scene‑level exposure slots, simultaneously maximizing total views and halving CTR variance while outperforming manual baselines.
This article presents the design, challenges, and experimental evaluation of DSTN (with pooling, self‑attention, and interactive‑attention variants) and MA‑DNN models for CTR prediction, highlighting how temporal and contextual ad information improves accuracy and yields significant online gains in large‑scale advertising systems.
The article provides a comprehensive overview of KDD 2017, including acceptance statistics, best paper awards, Ant Group's contributions, detailed discussions on AB testing, graph mining, and selected research papers across data mining, machine learning, and anomaly detection, offering valuable insights for practitioners and researchers.
Data mining, an interdisciplinary field of computer science, involves tasks such as anomaly detection, clustering, classification, and regression, follows standardized processes like KDD, CRISP-DM, and SEMMA, and often leverages machine learning techniques—including supervised, unsupervised, and reinforcement learning—to extract valuable insights from complex datasets.