CTR预估论文精读(十) - 程序员宅基地

CTR预估论文精读(十五)--MMOE: Multi-gate Mixture-of-Experts

MMOE: Modeling Task ...0. 论文摘要 Neural-based multi-task learning has been successfully used in many real-world large-scale applications such as recommendation systems. For example, in movi

CTR预估论文精读(十六)--Youtube 排序系统：Recommending What Video to Watch Next

标签： MMOE bias network 推荐系统

1. 摘要 In this paper, we introduce a large scale multi-objective ranking system for recommending what video to watch next on an industrial video sharing platform. The system faces many real-world ...

ctr预估公开数据集 avazuavazu-ctr-prediction

标签：数据集 ctr 点击率预估

ctr预估公开数据集 avazu avazu-ctr-prediction ctr预估公开数据集 avazu avazu-ctr-prediction ctr预估公开数据集 avazu avazu-ctr-prediction ctr预估公开数据集 avazu avazu-ctr-prediction ctr预估公开数据集 ...

CTR预估论文精读(十)--xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender ...

标签： xDeepFM CTR预估论文精读(十) CIN

以xDeepFM为引，整理一下CTR深度学习模型的发展脉络，并对xDeepFM做具体解读。

CTR预估论文精读(八)--Deep Interest Network for Click-Through Rate Prediction

标签： DIN CTR Deep Interest Network for Click-Thr

系统总览2.1 电商CTR数据特点2.2 特征处理(User Behavior Features)2.3 **BaseModel**2.4 **Deep Interest Network**3. 训练技术3.1 Mini-batch Aware Regularization3.2 Dice: Data Dependent Activati...

CTR预估论文精读(十四)--Deep Rerank: Personalized Re-ranking for Recommendation(PRM)

标签： re-rank 重排 deep rerank

Deep Rerank: Personalized Re-ranking for Recommendation 论文解读 0. 论文摘要 Ranking is a core task in recommender systems, which aims at providing an ordered list of items to users. Typically, a ...

CTR预估论文精读(十三)--Behavior Sequence Transformer for E-commerce Recommendation in Alibaba(BST)

标签：机器学习深度学习推荐系统

Behavior Sequence Transformer for E-commerce Recommendation in Alibaba 论文解读 0. 摘要 In this paper, we propose to use the powerful Transformer model to capture the sequential signals underlying ...

CTR预估论文精读(十七)--ESMM: Entire Space Multi-Task Model

标签： ESMM ctr cvr

ESMM: Entire Space Multi-Task Model: An Eﬀective Approach for Estimating Post-Click Conversion Rate 论文总结本文介绍阿里妈妈团队发表在 SIGIR’2018 的论文《Entire Space Multi-Task Model: An Eﬀ...

CTR预估论文精读(十二)--Deep Session Interest Network(DSIN)

标签：深度学习机器学习 tensorflow

We observe that user behaviors are highly ... Based on this observation, we propose a novel CTR model named Deep Session Interest Network (DSIN) that leverages users’ multiple historical sessions i

20篇最值得一读的深度推荐系统与CTR预估论文

作者 |深度传送门转载自深度传送门（ID: gh_5faae7b50fc5）微博上近日流传一个段子，“2020年曾是各大科幻片中遥远的未来，但是现在离这个遥远的未来也...

CTR预估论文精读(一)--XGBoost

标签： XGBoost XGBoost论文 GBDT

XGBoost 论文阅读总结 1. 介绍 XGBoost的可扩展性(scalability)归因于一些重要的系统优化和算法优化。这些优化包括：一种新的tree-learning算法(a novel tree learning algorithm)：用于处理稀疏数据(sparse data)...

CTR预估论文精读(九)--Deep & Cross Network for Ad Click Predictions

标签： CTR预估 Deep &amp; Cross Network for Ad Click P DCN

摘要 Feature engineering has been the key to the success of many prediction models. However, the process is nontrivial and often requires manual feature engineering or exhaustive searching....

CTR预估论文精读(三)--Factorization Machines

标签： FM Factorization Machines FM论文总结

Abstract In this paper, we introduce Factorization Machines (FM) which are a new model class that combines the advantages of Support Vector Machines (SVM) with factorization models....

CTR预估论文精读(十一)--Deep Interest Evolution Network(DIEN)

标签：机器学习人工智能深度学习

Deep Interest Evolution Network(DIEN) 论文解读 0. 摘要 Deep Interest Evolution Network (DIEN) uses interest extractor layer to capture temporal interests from history behavior sequence. At this layer,...

CTR预估论文精读(四)--Field-aware Factorization Machines for CTR Prediction

标签： Field-aware Factorization Machines FFM论文阅读总结 FM

Field-aware Factorization Machines for CTR Prediction 1. FM FM的具体详情可以参考之前的论文总结Factorization Machines 论文阅读总结 1.1 FM是什么将矩阵 W=wi,jW={w_{i,j}}W=wi,j 矩阵（这是一个对称方阵）...

CTR预估论文精读(二)--LightGBM

标签： LightGBM GBDT XGBoost

1. Abstract 在之前我介绍过XGBoost，...LightGBM论文的标题为A Highly Efficient Gradient Boosting Decision Tree。这说明LightGBM它是对于XGB提升性能的版本。而LightGBM相对于其他GBM来说具有相近的准确率而且...

CTR预估论文精读(七)--DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

标签： DeepFM DeepFM 论文阅读总结 DeepFM: A Factorization-Machine bas

Abstract Learning sophisticated feature interactions ...user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias to...

CTR预估论文精读(五)--Practical Lessons from Predicting Clicks on Ads at Facebook

标签： Practical Lessons from Predicting C GBDT+LR GBDT+LR paper

Abstract Online advertising allows advertisers to only bid and pay for measurable user responses, such as clicks on ads. As a consequence, click prediction systems are central to most online advertisi...

CTR预估论文精读(六)--Wide & Deep Learning for Recommender Systems

标签： Wide &amp;amp;amp;amp;amp; Deep Wide &amp;amp;amp;amp;amp; Deep Learning for Recommende wide and deep 论文笔记

Wide &amp;amp;amp;amp;amp;amp;amp; Deep Learning for ... Deep Learning for Recommender Systems 论文阅读总结Abstract1. Introduction1.1 Memorization &amp;amp;amp;amp;amp;amp;amp; Generalizat

【多场景建模】CIKM2021 阿里妈妈多场景CTR预估模型STAR 论文精读

标签：大数据算法 python

文|Youngshell审|bindingdai多场景建模是推荐系统CTR预估领域比较热门的一个研究方向，本次给大家分享的论文是：《One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction》...

搜索和CTR预估入坑指南

标签：搜索引擎 CTR预估

搜索小白、CTR小白，整理了一份入坑指南，一些比较好的博客和论文。本文适用于有一定的机器学习和深度学习基础，但是不懂搜索和CTR的同学。

CTR预估论文实践(四)--Field-aware Factorization Machines for CTR Prediction

标签： CTR预估 FFM 实践 Field-aware Factorization Machines

FFM 模型实践代码是用python3.5写的，tensorflow的版本为1.10.1；博客代码均以上传至GitHub，欢迎follow和start~~! 1. 数据集数据集地址：MovieLens 本文使用的数据是movielens-100k，数据包括u.item，u.user，ua....

CTR预估论文实践(九)--Deep & Cross Network for Ad Click Predictions

标签： CTR预估论文实践(九) Deep & Cross Network for Ad Click P DCN

Wide &amp;amp; Deep 模型实践博客代码均以上传至GitHub，欢迎follow和start~~! 1. 数据集数据集地址：...判定是否需要上交索赔，是二分类问题；评价指标本实验采用 Logloss。...2....

【推荐系统多任务学习 MTL】PLE论文精读笔记（含代码实现）

标签： PLE 多任务学习 MTL

论文地址： Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model ...PLE 是 MMoE （详见【推荐系统多任务学习MTL】MMOE论文精读笔记（含代码实现））的改进版，结构简单且效果好，PLE 主

【论文解读】YouTube采样修正的双塔模型论文精读

标签：算法大数据编程语言

「本文概览」《Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations》是谷歌在2019年...

【推荐系统多任务学习MTL】MMoE论文精读笔记（含代码实现）

标签： MMOE 推荐系统深度学习

阿里团队提出 ESMM 模型利用 MTL 的方法极大地提升了 CVR 预估的性能，同时解决了传统 CVR 模型预估的一些弊病。我们从模型的网络结构可以了解到，ESMM 是典型的 share-bottom 结构，即底层特征共享方式。这种 MTL ...

DMTL论文精读

标签：推荐算法 DMTL 蒸馏

大部分候选集生成模型都是基于CTR预估构建的，没有考虑用户点击之后的满意度。一些标题党item可能被推荐给用户，从而破坏用户体验。解决这个问题的一个方法就是同时给用户点击和用户阅读时长构建多任务学习模型。该...

【推荐系统多任务学习MTL】ESMM 论文精读笔记（含代码实现）

标签： ESMM 推荐系统推荐算法

论文地址：Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate 一、前言点击率（Click-Through Rate，CTR）和转化率（Conversion Rate，CVR）在信息检索、推荐系统...

CTR预估模型（FM/DeepFM/AFM/DCN/DIN）

DeepFM DeepFM: A Factorization-Machine based Neural Network for CTR Prediction 论文精读

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