This article details Meituan Waimai's end‑to‑end interactive recommendation system for the food‑delivery homepage feed, explaining its architecture, trigger strategies, recall and ranking pipelines, evaluation metrics, experimental results, and future optimization directions.
This article explains the architecture of a video search ranking system that combines a deep encoding network, multi‑task expert networks, and a bias‑correction module to jointly optimize relevance, click‑through rate, and watch time for streaming platforms.
This talk presents the evolution of QQ Music's ranking system, detailing background challenges, user‑perception modeling, multi‑objective and causal learning to mitigate the Matthew effect, long‑tail content support, cross‑domain recommendation, and module personalization for diversified traffic, concluding with future research directions.
This talk details the evolution of QQ Music's ranking system, covering background, user‑perception modeling, pairwise optimization, advanced model architectures, multi‑objective learning with causal inference to mitigate the Matthew effect, cross‑domain recommendation, and module personalization that together boost user engagement and platform traffic.
The article presents the Deep Match to Rank (DMR) model, which integrates collaborative‑filtering inspired user‑to‑item relevance modeling into the ranking stage of recommendation systems, achieving significant offline and online improvements in click‑through rate and revenue metrics for e‑commerce platforms.
This article examines iQIYI's end‑to‑end personalized poster production and distribution system, detailing AI‑driven image cropping, smart frame extraction, feature extraction, multi‑armed bandit ranking, and online experiments that together significantly increased poster click‑through rates on TV and mobile platforms.
This article examines the differences and similarities between classification (pointwise) and ranking (pairwise) models for recommendation systems, covering their probabilistic foundations, loss functions, parameter updates, and practical implications such as sensitivity to statistical features and robustness.
This article introduces the ListNet listwise ranking algorithm, explains its theoretical foundations and loss function, presents a Python implementation with gradient computation, and demonstrates its superior performance over pointwise and pairwise models on public benchmarks and an internal automotive dataset for predicting users' intended car series.
This article outlines Meituan’s end‑to‑end recommendation system, describing its data layer, candidate‑generation triggers, fusion strategies, and machine‑learning‑based ranking models—including collaborative filtering, location‑based, query‑based, graph‑based methods, and both linear and non‑linear models—while highlighting practical optimizations such as AB testing, real‑time behavior handling, and fallback strategies.
