Personalized Delivery of On-Line Search Advertisement Based on User Interests Guangyi Xiao, Zhiguo Gong University of Macau

Advertising in Google II

Search advertising becomes the main revenue share  Internet advertising revenues (“revenues”) in the United States $23 billion in 2008, 10.6% increase comparing with 2007  Despite a difficult U.S. economy, internet advertising’s continued growth  Search 45% in 2008, 42% in 2007

Object of advertising auction  maximize revenues,  efficiency  and user satisfaction.

Payment scheme in online advertising  cost-per-thousands-impressions (CPM) model  cost-per- click (CPC) model  cost-per-event (CPE) model (e.g. Sales)

Two kinds of ranked framework for advertising  Ranked by bid, adopted previously by Yahoo  Ranked by revenue, adopted by Google.  In this paper, maximizing revenue is the objective for advertisement scheduling.

Three Auction Scheme  First Price Scheme (It is very simple.)  Second Price Scheme, used in our system.  VCG (Vickery Clarke Groves), charge bidder i according to the overall loss of all others because of i’s presence

Advertisement Ranking  Ranked by bid value  A1>A2>…>An  Ignore if the adv be clicked  Ranked by bid value, together with ads quality  A 1 Q 1 >A 2 Q 2 >…>A n Q n  Ignore if the individual user clicks it  Ranked by bid value, together with user’s interest  A 1 I 1 >A 2 I 2 >…>A n I n

Revenue  Limited space for advertising  Select only several ads to display  Second price scheme is used  Only m ads can be displayed

Revenue (cont.)

Interest Model 1.f 0 (u,j), user u’s query sessions comparing ads j’s bid terms 2.f 1 (u,j), user u’s overall history query terms comparing with j’s bid terms 3.f 2 (u,j), user u’s view of j in the history 4.f 3 (u,j), user u’s click of j in the history

Experiment

An Example of Our Ads Scheduling