Search Engine Advertising

Keyword Generation for Search Engine Advertising : 

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

Search Results : 

Search Results Sponsored Search Results

Long Tail : 

Long Tail Queries Frequency in query-logs

Keyword Pricing : 

Keyword Pricing

Pick the right keywords : 

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

Objective : 

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.

Who’s doing all this? : 

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

Other Techniques … : 

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

Other Techniques … : 

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

Other Techniques : 

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

Directed Relevance Relationships : 

Directed Relevance Relationships Word A strongly suggests word B, but the reverse may not hold true Example:

Building Context : 

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

Building the Graph : 

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

TermsNet : 

TermsNet

Ranking Suggestions : 

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’

Ratings : 

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

Evaluation : 

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

Output for query ‘flights’ : 

Output for query ‘flights’

Avg. Precision, Recall, Nonobviousness : 

Avg. Precision, Recall, Nonobviousness

Evaluation Measures : 

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

F-Measures : 

F-Measures

Quality of Suggestions over different intervals of ranked results : 

Quality of Suggestions over different intervals of ranked results

Future Directions : 

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

Thank You! : 

Thank You! Questions? amrutaj@cs.stanford.edu