Solutions and Challenges to Computation Advertising MediaV Peng 北冥乘海生

Brand Advertising Create a distinct favorable image

Direct Marketing Advertising that involves a "direct response" : buy, subscribe, vote, donate, etc, now or soon.

Audience targeting Which is more effective?

Advertising effectiveness exposure attention comprehension message acceptance retention purchase stageprinciples 1.1. Nature of the ad position 2.1 Don’t interrupt user’s task 2.2 Disclose the recommendation reason 2.3 Match user’s interests or needs 3.1 Convey message in user’s interest scope 3.2 Attention-aligned understanding barrier 4.1 Brand and creative recognition 4.2 Publisher/channel recognition 5.1 artistic quality 6.1 Set appropriate barrier for price- sensitive users selection interpre -tation attitude

Targeting technologies exposure attention comprehension message acceptance retention purchase stage targeting contextual (2.1, 3.1) re-targeting (2.2, 2.3, 3.1) behavioral (2.3, 3.1) geo (2.3, 4.1) demographical (2.3, 3.1, 6.1) channel (2.3, 3.1, 4.2) effectiveness applicable stages hyper-local (2.3, 4.1) look-alike (2.3, 3.1, 4.1, 6.1) group purchase (2.3, 4.1, 6.1)

ROI & computational advertising ROI of an advertising market –Investment = #X ⅹ CPX; –Return = #impression ⅹ CTR ⅹ click value = #impression ⅹ e(xpected)CPM CPM market: static eCPM CPC market: dynamic CTR, static click value CPA/CPS/ROI market: dynamic CTR and click value Key problem: eCPM estimation –Who: Various market place designs –How: Computational advertising

Ad network Ad network: –Connects advertisers to web sites that want to host advertisements –Estimate CTR and Matching ad with (context, user) by itself –Charge advertisers with CPC, CPM or other contracts –Hard to support various audience segments

Ad exchange Mission –Platforms that facilitate the bided buying and selling of online media ad inventory from multiple ad networks Key features: –Bridging ad with (context, user) by real time bidding (RTB) –Charge advertisers with real time bids on impressions.

Demand side platform Mission: –Allows digital advertisers to manage multiple ad exchange and data exchange accounts through one interface Key features: –Demand defined audience segments –Cross-media traffic acquisition –Evaluate advertiser ROI to support RTB

An ad flow illustration DSP 1 Media 1 Media 2 Media 3 Ad net 1 Ad net 2 Adx DSP 2 Agency 1 ATD Advertiser SSP Agency 1

Display ad market place

Computational advertising Main challenge –Find the best match between a given user in a given context and a suitable advertisement. Examples: –Context = Web search results -> Sponsored search –Context = Publisher page -> Content match, banners –Other contexts: mobile, video, newspapers, etc

Essentials A scientific sub-discipline at the intersection of –Web-scale search and text analysis –Information retrieval –Statistical modeling and machine learning –Numerical optimization –Microeconomics –Recommender systems The goal is to satisfy both quality and quantity requirements in ad serving.

Key messages A principled way to find the "best match" between a user in a context and a suitable ad. The financial scale for comp. advertising is huge –Small constants matter –Expect plenty of further research Advertising is a form of information –Adding ads to a context is similar to the integration problem of other types of information –Finding the “best ad” is an information retrieval problem with multiple, possibly contradictory utility functions

A skeleton of advertising system Ad retrieval User Context Ad candidates Freshness Popularity Quality … result Explore & Exploit User response optimization Relevance ranking Offline click feedback Online click feedback

Conceptual architect Ad server Storm Zookeeper CTR Model Audience Targeting Anti -spam Realtime CTR feedback User Profiling Realtime Billing Cache Thrift + Scribe

Comparison between several web scale applications SearchSponsored search contextual ads Display adsRecommen -dation Top CriteriarelevanceROIuser interest Other aspects application dependent qualitydiversity, freshness Index scale~billions~dozens of millions~millions~hundreds of millions User scaleshallow personalization~billions Contextualinsensitivesensitive Retrieval signals concentratedrichdemand side defined rich Downstream optimization not applicableapplicable

CTR prediction Challenges: –Dynamics: high churn rate of items, rapid evolution of user interests –Scale: Billions of training samples, and online features Offline CTR modeling –Click prediction model trained with offline data: –It is a regression problem rather than a ranking problem –Click feedback features for dynamic signal capturing –Logistic regression in widely adopted. Online CTR modeling

Logistic regression Objective –p (click | ad, user, context) = 1 / (1 + e -z ) (z = w 1 f 1 + w 2 f 2 + … + w N f N ) Optimization: L-BFGS, Trust-region Map-reduce –Mappers: derivative computation –Reducers: Parameter update Weakness: –Can hardly model non-linear behavior patterns

Click feedback Effective impression –EC: Expected Click, the sum of reference CTR for each impression. Normalized impression number Reference CTR –A CTR model for different presentation biases. Normalized CTR –COEC: Click Over EC = Click / EC. equally viewed?

Major bias features Position of ad space Size of ad space File size of creative Type of ad space (Homepage, channel homepage, content page, client, etc.) Type of creative (Picture, flash, rich-media) Mobile or desktop Browser & OS Time & Date

Multiple level click feedback Impression triple: (user, ctxt, ad) Multi-level descriptors: –User: cookie, gender, interest, city, … –Ctxt: url, topic, domain, channel, … –Ad: banner, solution, campaign, advertiser, topic Segment traffic by 1-3 descriptor combos, and use the statistics as features. –Current: ck, (ck, banner), (city, banner), … –Future: (interest, ad), (domain, ad topic), …

CTR smoothing Goal: Reliably estimate CTR with sparse data. A Empirical Bayesian treatment: Click generation: Treat as a random, and apply a conjugate prior to regularize it: How to make use of item hierarchy for better regularization?

Evaluation PR curve ROC curve AUC

Apply linear model in retrieval Baseline ad selection methodology –1. Retrieve all feasible candidates. –2. Apply ranking model to each feasible one and select top K. How can we handle a large feasible set? –If the model is linear, the ad selection problem can be view as a two step process: Calculate S = F ⅹ W (S :scores, F : features, W : weights) Select top K socres from S. –With elegant index and algorithm design, We can merge the two steps and selected top K results in a parsimonious way.

Weighted AND algorithm [A. Broder, etc. 2003] Index –Enhanced reverted index, with keeping all the documents sorted by their contribution to the score on the index key. Retrieval –Estimate the upper bound of a (query, document) given the visited features on the fly, and prune candidates as early as possible. –The algorithm can make sure infeasible candidates not touched in retrieval, and only part of the weighted sum operator need to be calculated.

Click value estimation Applications: –Real time bidding for DSP –Bidding tool for ad network –Smart price Challenges: –Extremely sparse training data –Advertiser-dependent behavior modes Principle in click value prediction –Aim at a smooth modeling approach with small variance but relatively large bias

Stream computing for online click feedback: S4 as an example

Explore & Exploit Goal: –Assign tail items adequate changes to obtain sufficient statistics –A multi-arm bandit (MAB) problem. Approaches: –ε–greedy: A small random traffic for exploration –Upper Confidence Bound: Play the arm with largest upper bound of expected return [J. Audibert, 2006] Challenge: –Large number of items to be explored.

ROI with quantity constraint Goal: –Optimize ROI w.r.t. a given ad spend in one or more ad networks. Examples: SEM, ATD Two key problems: Set solutions on appropriate traffic segments (keywords in SEM, labels in ad network). Bid on each solution to optimize the entire ROI. State-of-the-art: Portfolio selection theory. [H. Markowitz, 1959]

Portfolio and Efficient frontier Treat the group of keywords/solutions as a portfolio of stocks, and find the efficient frontier to optimize ROI given an ad spend.