Personalized Delivery of On-Line Search Advertisement Based on User Interests

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

2. Advertising in Google • I

3. 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 • Search45% in 2008, 42% in 2007

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

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

6. 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.

7. 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

8. Advertisement Ranking • Ranked by bid value • A1>A2>…>An • Ignore if the adv be clicked • Ranked by bid value, together with ads quality • A1Q1>A2Q2>…>AnQn • Ignore if the individual user clicks it • Ranked by bid value , together with user’s interest • A1I1>A2I2>…>AnIn

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

10. Revenue (cont.)

11. Interest Model f0(u,j), user u’s query sessions comparing ads j’s bid terms f1(u,j), user u’s overall history query terms comparing with j’s bid terms f2(u,j), user u’s view of j in the history f3(u,j), user u’s click of j in the history

12. Experiment

13. An Example of Our Ads Scheduling