Keyword Generation for Search Engine Advertising

1. Keyword Generation for Search Engine Advertising Amruta Joshi*, Yahoo! Research Rajeev Motwani, Stanford University * This work was done at Stanford Amruta Joshi and Rajeev Motwani, Stanford University

2. Search Results Sponsored Search Results Amruta Joshi and Rajeev Motwani, Stanford University

3. Expensive, high frequency keywords Target inexpensive, low frequency keywords instead Long Tail Frequency in query-logs Queries Amruta Joshi and Rajeev Motwani, Stanford University

4. Keyword Pricing Amruta Joshi and Rajeev Motwani, Stanford University

5. Pick the right keywords • Advantages • more focused audience • lesser competition, easier to get #1 position • cost-effective alternative • Keywords should be • Highly Relevant to base query • Nonobviousness to guess from the base query • E.g.: • hawaii vacation $3 • kona holidays $0.11 Amruta Joshi and Rajeev Motwani, Stanford University

6. Objective • To generate, with good precision and recall, a large number of keywords that are relevant to the input word, yet non-obvious in nature. Amruta Joshi and Rajeev Motwani, Stanford University

7. Who’s doing all this? • Large Advertisers • SEO companies and small start-ups manage advertising profiles • Eg: www.adchemy.com, www.wordtracker.com, http://www.globalpromoter.com • Eventually every advertiser is interested in optimizing his portfolio Amruta Joshi and Rajeev Motwani, Stanford University

8. Other Techniques … • Meta-tag Spidering: • Extract Keyword & Description tags from top search hits • Example of meta-tags for query ‘hawaii travel’ • Relevant: hawaii travel, hawaii vacation, hawaiian islands, hawaii tourism • Off-topic: hawaii homes, moving to hawaii, hawaii living, hawaii news, living in hawaii, hawaii products, • Irrelevant: sovereignty, volcanoes, sports, music Amruta Joshi and Rajeev Motwani, Stanford University

9. Other Techniques … • Proximity-based tools • Pick phrases in the proximity of given word • e.g.: family hawaii vacations, discount hawaii vacations • Query log Mining • Suggest popular queries containing seed keywords Amruta Joshi and Rajeev Motwani, Stanford University

10. Other Techniques • Advertiser log mining or Query Co-occurrence based mining • Exploits co-occurrence in advertiser keyword search logs • Increase competition! Amruta Joshi and Rajeev Motwani, Stanford University

11. x ≠ y 2 25 y x B A A B railways eurail railways eurail Directed Relevance Relationships • Word A strongly suggests word B, but the reverse may not hold true • Example: Amruta Joshi and Rajeev Motwani, Stanford University

12. Search Engine europe. europe. C Building Context • Characteristic Document • Build context of the term using terms found in the proximity of seed term in the top 50 hits from search engine for that term Amruta Joshi and Rajeev Motwani, Stanford University

13. Building the Graph • TermsNet • Nodes = terms • Edges = directed relevance relationships • Weights = strength of directed relationship, i.e., the frequency of destination term in characteristic document of source term Amruta Joshi and Rajeev Motwani, Stanford University

14. 25 32 14 30 19 15 eurail europe . euro railways atlas schengen maps C C C C C C C TermsNet Amruta Joshi and Rajeev Motwani, Stanford University

15. wx,q x q Ranking Suggestions • Quality Score Incorporates • Edge-weights • Normalization for common words Quality Q(x, q) = wx,q / (1+log (1+∑wx,i)) where each i is an outneighbor of ‘x’ Amruta Joshi and Rajeev Motwani, Stanford University

16. Ratings • Relevance • Indicates Relevance of suggested keyword to seed word • Given by human editors • e.g.: For query ‘flights’ • Relevance (‘flights’, ‘cathay pacific’) = 1 • Relevance (‘flights’, ‘cheap flight’) = 1 • Relevance (‘flights’, ‘magazines’) = 0 • Nonobviousness • Indicates nonobviousness of suggested keyword relative to seed word • Calculated as: • If No base query word/stem present in suggested keyword, Nonobviousness = 1, else = 0 • e.g.: For query ‘flights’ • Relevance (‘flights’, ‘cathay pacific’) = 1 • Relevance (‘flights’, ‘cheap flight’) = 0 • Relevance (‘flights’, ‘magazines’) = 1 • Used standard Porter stemmer for automating this rating Amruta Joshi and Rajeev Motwani, Stanford University

17. Evaluation • Evaluation Measures • Average Precision: • Ratio of number of relevant keywords retrieved to number of keywords retrieved. • Indicates quality of results • Average Recall • The proportion of relevant keywords that are retrieved, out of all relevant keywords available. • For our expts Recall (Ti) = # retrieved by Ti / # retrieved by (T1 U T2 U…U Tn) • Average Nonobviousness • Average of all nonobviousness ratings of suggested keywords Amruta Joshi and Rajeev Motwani, Stanford University

18. Output for query ‘flights’ Amruta Joshi and Rajeev Motwani, Stanford University

19. Avg. Precision, Recall, Nonobviousness Amruta Joshi and Rajeev Motwani, Stanford University

20. Evaluation Measures • F-measures • Measure of overall performance • Harmonic mean of • F(PR) – Avg. Precision & Avg. Recall • F(RN) – Avg. Recall & Avg. Nonobviousness • F(PN) – Avg. Precision & Avg. Nonobviousness • F(PRN) – Avg. Precision, Avg. Recall & Avg. Nonobviousness Amruta Joshi and Rajeev Motwani, Stanford University

21. F-Measures Amruta Joshi and Rajeev Motwani, Stanford University

22. Figure 2: Quality of keywords over different ranked intervals Quality of Suggestions over different intervals of ranked results Amruta Joshi and Rajeev Motwani, Stanford University

23. Future Directions • Incorporate keyword frequency in ranking suggestions • Incorporate keyword pricing information in ranking suggestions • Applications to other domains • Find related movies, papers, people Amruta Joshi and Rajeev Motwani, Stanford University

24. Thank You! • Questions? • amrutaj@cs.stanford.edu Amruta Joshi and Rajeev Motwani, Stanford University