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Found 9 papers, 2 papers with code
PPM : A Pre-trained Plug-in Model for Click-through Rate Prediction
However, the explosive growth of online latency can be attributed to the huge parameters in the pre-trained model.
Rethinking Large-scale Pre-ranking System: Entire-chain Cross-domain Models
Industrial systems such as recommender systems and online advertising, have been widely equipped with multi-stage architectures, which are divided into several cascaded modules, including matching, pre-ranking, ranking and re-ranking.
Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction
Streaming data has the characteristic that the underlying distribution drifts over time and may recur.
CBNet: A Plug-and-Play Network for Segmentation-Based Scene Text Detection
Recently, segmentation-based methods are quite popular in scene text detection, which mainly contain two steps: text kernel segmentation and expansion.
PCDF: A Parallel-Computing Distributed Framework for Sponsored Search Advertising Serving
In this work, we propose a novel framework PCDF(Parallel-Computing Distributed Framework), allowing to split the computation cost into three parts and to deploy them in the pre-module in parallel with the retrieval stage, the middle-module for ranking ads, and the post-module for re-ranking ads with external items.
On the Adaptation to Concept Drift for CTR Prediction
Click-through rate (CTR) prediction is a crucial task in web search, recommender systems, and online advertisement displaying.
Telepath: Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems
This paper proposes Telepath, a vision-based bionic recommender system model, which understands users from such perspective.
LADDER: A Human-Level Bidding Agent for Large-Scale Real-Time Online Auctions
We present LADDER, the first deep reinforcement learning agent that can successfully learn control policies for large-scale real-world problems directly from raw inputs composed of high-level semantic information.
Optimizing Gross Merchandise Volume via DNN-MAB Dynamic Ranking Paradigm
With the transition from people's traditional `brick-and-mortar' shopping to online mobile shopping patterns in web 2. 0 $\mathit{era}$, the recommender system plays a critical role in E-Commerce and E-Retails.
