US group Machine Learning Practice (4) to calculate advertising

table of Contents

Advertising and marketing under Chapter 11 O2O scene

11.1 O2O advertising features in the scene

11.2 Merchant, balance between the interests of users and platforms

11.3 O2O advertising mechanism design

11.4 O2O ad push

11.5 O2O Advertising System Tools

Chapter 12 user preferences and loss modeling

12.1 How to define user preferences

Exchange value of 12.2 Advertising and preferences loss


Advertising and marketing under Chapter 11 O2O scene

11.1 O2O advertising features in the scene

Mobile, localization, scene of diversity

 

11.2 Merchant, balance between the interests of users and platforms

Merchant effect perception

user experience

Platform benefits

 

11.3 O2O advertising mechanism design

Advertising setting

Advertising recall mechanism

Advertising sorting mechanism

 

11.4 O2O ad push

Audience targeting: time orientation, redirect and strategic orientation classes, attributes orientation population, behavioral targeting, recommend new customers

 

11.5 O2O Advertising System Tools

System Tools for developers

Tools for advertisers and operations staff

 

Chapter 12 user preferences and loss modeling

12.1 How to define user preferences

User preferences Category: district, address location, category, price, and time; + short-term long-term

How to measure user preferences: orders> Click> Exposure

Different POI preferences

Users measure different POI preferences: Pointwise, Pairwise, Listwise

 

Exchange value of 12.2 Advertising and preferences loss

(1) optimization goals: both 3 Fangli Yi

(2) Modeling

Behavior: exposure value, click the value, the value of orders

Pointwise model of learning: GBRank, RankNet

                 

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Origin blog.csdn.net/weixin_41770169/article/details/93600431