Research Paper read - Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

Research Paper read - Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

Key Notes

• RTB - Real-time bids. The mechanism to buy and sell ads
• Key components - Demand-side platform, Supply-side platform, Real-time bidding
• Input Signals - Image, Video, Audio

Online Advertising Ecosystem
The different components and interaction is displayed in below picture

Realtime behavioral targeting

• Collect all traits
• Monitor and Alert
• Bid and reach out with relevant ads

User tracking
A user is typically identified by an HTTP cookie, designed to allow websites to remember the status of an individual user, including remembering shopping items added in the cart in an online store or recording the user’s previous browsing activities for generating personalized and dynamical content

Personalized workflow

This is an interesting pic. How many cookies present in NYT page. Cookie Syncing is done to keep track/sync all cookies of a particular user.

ML Use Case for Click-through rate prediction

Look-alike modeling - on the basis of the learned user profiles, identify and target unknown users who  have similar interests and commercial intents with the known (converted) customers

Conversion over multiple touchpoints

Key Concepts
CTR, Click-Through Rate - the probability of a specific user in a specific context clicking a specific ad
CVR, Conversion Rate - the probability of the user conversion is observed after showing the ad impression

Keep Thinking!!!

Research Paper Reads - MODELING USERS FOR ONLINE ADVERTISING

Paper #1 - MODELING USERS FOR ONLINE ADVERTISING

Key Notes

• Contribution - a neural network model (app2vec) to vectorize mobile apps by studying how users employ these apps

Data Collected from Users

• User activity data
• User behaviors
• Logging user activities
• Contents consumed by users
• Anonymous browser cookie syncing technique

Ad Platforms

• Targeting audiences
• User profiling
• Ads based on their activity history across the web

Findings

• Users watching polymorphic videos are likely to have similar interests

Insights

• US mobile users download more than eight apps per month on average
• 90% of the time spent on mobile devices was spent using apps

Online Ad Targeting

• Data - users browsing, app usage,
• and other activities on the Internet
• Targeting - site/page context, placement size, user behavior and geolocation

User Targeting
Publishers, Advertisers, Ad-networks, Online users

Research Directions

• Cross-device user tracking - Users access online content through multiple devices
• Value of user profile - Different costs associated with them, Ad targeting on user profile

Observe User Online Advertising Profile and Ad Targeting
Do ads target user profiles in the field?
What are the ads shown to different users?
How do ads impact users profiles?

Data - The capability to gather display ads and video ads from across the web is central to our work
Profile-driven crawling - Enables each crawler instance to interact with the ad ecosystem as though it were a unique user with particular characteristics.
The Anatomy of Online Advertising

• Advertisers - Advertiser reach out to potential customers. 
• Publisher View - premium campaigns (specific advertisers, ad networks, ad exchanges)

Types of ads - Text Ads, Display Ads, Stream Ads, Video Ads
Video ads - Pre-roll, mid-roll, post-roll, Overllay-ads, Sponsored Videos

User Modeling on Mobile

• app2vec to represent apps in a vector space without a priori knowledge of their semantics
• app2vec to cluster apps based on app distances in their vector space
• Computing app similarity is through the bag-of-words method using app meta information

Large Scale Look-alike Audience Modeling

• A simple similarity-based look-alike system can use direct user-2-user similarity  to search for users that look like (or in other words, be similar to) seeds
• Another type of look-alike audience systems for online advertising is built with Logistic Regression (LR)
• User segments can be user characteristics such as user interest categories. 

Real-time Attention Based Look-alike Model for Recommender System
Key Notes

• Real-time attention based look-alike model (RALM) for recommender systems
• Deep neural networks (DNNs) and recurrent neural networks (RNNs) are more and more popular on recommendation task
• "Matthew effect" - low quality and poor diversity of recommended contents.

RALM

• RALM is a similarity based look-alike model, which consists of user representation learning and look-alike learning
• Deep interest network for multifields user interests representation learning
• Local representation of seeds should be processed online in real-time
• k-means clustering to partition seeds into k clusters
• Similarity based methods determine similarity between seeds and users based on distance measurement.

System Architecture
Offline Training

• User Representation learning. The user representation model is developed based on deep learning network
• Look-alike learning is based on attention model and clustering algorithm

Online asynchronous processing

• User feedback monitor: The audience extension system updates the seeds of candidates through monitoring the click behaviors of all WeChat users in real-time
• online serving - The lookalike model predicts the global embedding of seeds through global attention unit

Metrics

• CTR (Click-through Rate): As audience increased, many new users sharing the same interests with seeds are reached. Therefore, CTR is expected not to decrease
• Category & Diversity. One of our purposes is enriching user’s interest in our system, so we define a metric named diversity. It is represented by a number of content categories or tags a user has read in a day. With a more comprehensive user representation, more kinds of contents will be reached and category&tag diversity is expected to increase

More Reads
Comprehensive Audience Expansion based on End-to-End Neural Prediction

Keep Thinking!!!