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2015-04 eBay Statistics
1. Welcome To
Director of Engineering
Search Science Recall & Spam
April 23, 2015
BRIAN JOHNSON
With more than 100 million active users globally, eBay is
the world's largest online marketplace, where practically
anyone can buy and sell practically anything. Founded in
1995, eBay connects a diverse and passionate community
of individual buyers and sellers, as well as small
businesses. Their collective impact on ecommerce is
staggering: In 2014, the total value of goods sold on eBay
was $82 billion -- more than $2,500 every second.
16. METRICS
•What should we optimize
–Page Views
–Time on Site
–Click Through Rate
–Normalized Discounted Cumulative Gain
–Purchases per User per Session/Day/Week
–Revenue per User per Session/Day/Week
–Net Promoter Score
•How likely would you be to recommend …?
21. Jaccard Similarity
We see two sets A and B . There are three elements in their intersection and a total of eight elements that
appear in A or B or both. Thus, JaccardSimilarity(A, B) = 2/5.
I
Love
Statistics
Am
Learning
A B
22. Similar Item Titles
9 words overlap
4 words different
13 words total
Jaccard Similarity is 9/13 or 0.69
28. Features: Mutual Information
• Rationale: The goal of this metric is determine if the co-occurrence of
the candidates in the description is significantly more than the
random chance of them co-occurring.
29. Features: Neighborhood Similarity
• Rationale: Two synonym candidates A and B, will tend to
have similar neighbors (viz keywords) surrounding them.
Intersection ( Neighbours(A) , Neighbours(b) )
Min (Neighbours(a), Neighbours(b))
Neighborhood
similarity =
30. Features: KL divergence
• Rationale: Two synonym candidates will have similar
price and category distributions of their inventory.
Notas del editor
Why
What
How
http://www.ebayinc.com/who
You are in business to make money
How do you know if changes you make, make money
You HAVE to test
You can’t manage what you don’t measure
Testing is crucial
Image http://www.wallpapertimes.com/files/q/Yf/4j/qYf4jp9q86379020_800x600.jpg
(It was very hard to find a good example of this that brought in obviously wrong data above the fold: these issues are generally more subtle, showing up in deterministic sorts and in slower processing time. If you come up with another good example to include, that would be great.)
There are many entity names, including many brands, which are identical to (Cowboys) or share components with (e.g. Red Bull) common terms that describe our inventory. By identifying entities and by using whole query context, we can provide expansions only when appropriate (e.g. no Redder Bull or Crimson Bull). We can also decide the confidence of an expansion compared to the original (e.g. as is usually done in spell check).
For the cowboy(s) hats, Cowboys seems to mainly refer to the football team; there are a few cowboy hats where someone used “cowboys” instead of the possessive, but not many. For the toys, the plural form is definitely more common but the singular is also used in titles even in sets (bottom row of pictures has the singular; top row the plural); so, we want to use both forms to get the maximum inventory for this.