Keyword Generation for Search Engine Advertising

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Keyword Generation for Search Engine Advertising

• Amruta Joshi, Yahoo! Research
• Rajeev Motwani, Stanford University

This work was done at Stanford
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Search Results
Sponsored Search Results
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Long Tail
Frequency in query-logs
Queries
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Keyword Pricing
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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

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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.

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Whos 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

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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

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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

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Other Techniques

• Advertiser log mining or Query Co-occurrence
  based mining
• Exploits co-occurrence in advertiser keyword
  search logs
• Increase competition!

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Directed Relevance Relationships

• Word A strongly suggests word B, but the reverse
  may not hold true

• Example

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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

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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

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TermsNet
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Ranking Suggestions

• Quality Score Incorporates
• Edge-weights
• Normalization for common words

Quality Q(x, q) wx,q / (1log (1?wx,i))
where each i is an outneighbor of x
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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

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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 UU Tn)
• Average Nonobviousness
• Average of all nonobviousness ratings of
  suggested keywords

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Output for query flights
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Avg. Precision, Recall, Nonobviousness
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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

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F-Measures
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Quality of Suggestions over different intervals
of ranked results
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Future Directions

• Incorporate keyword frequency in ranking
  suggestions
• Incorporate keyword pricing information in
  ranking suggestions
• Applications to other domains
• Find related movies, papers, people

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Thank You!

• Questions?
• amrutaj_at_cs.stanford.edu