At Zappos, Algorithms Teach Themselves
July 08 2019 - 5:59AM
Dow Jones News
By Jared Council
Zappos LLC, an online seller of shoes and apparel, said a
self-learning algorithm has shown promise in solving one of its
most perplexing search-engine issues: irrelevant results.
The 20-year-old Las Vegas-based company, which Amazon.com Inc.
purchased in 2009, relies on its internal search engine to help
customers find what they're looking for. But for years, the search
engine would get tripped up by a variety of words and phrases. For
instance, in past searches for brand names that include a color --
such as "Red Wing boots" -- the search engine would see the color
and deliver results with that color -- in this case, red boots.
Searches for "dress shirt" would include dress shirts and
dresses.
Ameen Kazerouni, the company's lead data scientist, said his
team about two years ago started testing a decades-old technique
known as a genetic algorithm as a potential solution. The
self-learning algorithm started eliminating such search mishaps
within the first year, he said, and has become a key factor in
increasing the overall relevancy of its search engine.
"We've seen a decrease in repeated searches -- people trying to
add more and more words to the search term...and fewer clicks in
what it takes to find the right results," Mr. Kazerouni said. The
results imply "that it is easier to find stuff -- which is
something we've not been able to achieve before," he said.
Genetic algorithms produce various solutions to a problem and
use principles of natural selection, including reproduction and
mutation, with the goal of producing the optimal or "fittest"
solution. For instance, genetic algorithms can be used to help
logistics companies discover the best routes for delivery
drivers.
While genetic algorithms have been around since the 1970s,
advances in computing power and speed have increased their
appeal.
"For a long time, speed was the reason a lot of people didn't
use this stuff. Now as computers are starting to speed up...people
are starting to use techniques that beforehand were just too slow,"
said Eli Finkelshteyn, chief executive of Constructor.io, which
also uses genetic algorithms. The company sells search and
product-discovery software that powers e-commerce websites.
Unlike with traditional search engines, Mr. Finkelshteyn said,
those utilizing genetic algorithms in particular and
machine-learning algorithms in general can integrate user behavior
after a search as feedback. The system can use that feedback to
learn what's relevant for future searches, which can ultimately
boost sales.
In 2017, Zappos saw roughly a million unique terms searched on
its site each month. Its search engine had to match those terms
with products in its 100,000-plus item catalog. Tackling
search-result mistakes -- such as showing shorts when people search
for "classic short," a popular Ugg boot style -- seemed to be a
complex math problem, Mr. Kazerouni said. His team thought genetic
algorithms, which have been used for complex math problems in
logistics, telecom and other industries, could help.
The Zappos team built its system in house and designed it to
produce algorithms that could parse out the intent of a search
phrase. One algorithm might see the word "dress" as a strong signal
for fetching dresses, but another might play down that signal and
pay more attention to the surrounding words.
In that scenario, the algorithm that performs the best on an
internal "relevance test" developed by Zappos -- which simulates
how users engage with search results -- would have the greatest
chance of seeing its traits passed to the next generation. The
best-performing algorithm is placed live on the website until
another one performs better. The company wouldn't disclose metrics
around its search engine's performance, but it said the relevance
scores for its genetic algorithm initially were as low as 0.0001 on
a scale of zero to 1 and as high as 0.8 in the latest
generation.
The Zappos team later developed two other genetic algorithm
engines. The company uses all three in tandem to come up with
better search results.
"If a person wants to buy some shoes, and they include the word
'dress' and the site starts giving them dresses instead of dress
shoes, they're going to get fed up and go to a competitor," Mr.
Finkelshteyn said. "It's incredibly important to give users
relevant and appealing results so they stay on site and keep
looking."
(END) Dow Jones Newswires
July 08, 2019 05:44 ET (09:44 GMT)
Copyright (c) 2019 Dow Jones & Company, Inc.
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