John Berryman discusses the importance of automatic tagging in e-commerce search to enhance the customer experience and improve inventory categorization. He outlines various approaches to tagging, including curator involvement, content creator input, and customer contributions, while also presenting a machine learning-based method for leveraging search logs to create a tagging model. The presentation highlights challenges and potential improvements in tagging methods, the benefits of broader tags, and future directions for refining the process.

1. Search Logs
+ Machine Learning
= Automatic Tagging
John Berryman

2. Hi! I'm John Berryman
@JnBrymn
This is my name
with all the
unnecessary
letters removed.
● Degree in Aerospace Engineering
● Moved into Search Technology
● Wrote a book (... well 40% of one)
I got a haircut. Life
has been different
ever since.
That's me on
the cover.
● Discovery Engineer @ Eventbrite
(Search/Recommendations)

3. What is "tagging"?
… and why would you want it?

4. First, let's talk about e-commerce search
● Search is ubiquitous.
○ Search makes the internet accessible
○ Search is the backbone of many products
○ Search is embedded in most products
● E-commerce is powered by search
● Browse is an important aspect of
the experience. You filter inventory
based upon tags.
● Mobile users prefer browse over
text search.
● Everyone is moving to mobile.
These are tags!
These are
also tags!

5. What is "tagging"?
… and why would you want it?
It's the ability to CATEGORIZE and
UNDERSTAND your inventory.
Because it powers the emerging
dominant e-commerce interaction.

6. How can you tag your inventory?
● Use curators to tag content:
○ Benefits: control over tagging, uniform tagging approach
○ Drawbacks: curation approach must be define, curators must be trained, curators are
expensive
● Require tagging from content creators:
○ Benefits: content creators know their content the best, scales well
○ Drawback: content creators may not cooperate if they see no advantage for themselves
● Encourage customers to tag content:
○ Benefits: customers are the ones buying content and their idea of tags matters most
○ Drawbacks: there's even less likelihood for customers to cooperate
… but what of nobody wants to tag your content?

7. An Interesting Observation
● Every day millions of people search for events on Eventbrite.
● They issue approximately > 500K distinct queries in a month.
● But the most common 1,000 queries accounts for 41% of all search traffic.
● The common queries look like tags!
Can we use logged searches as a
training set to built a tagging model?
○ 5k run
○ back to school
○ job fair
○ 4th of july party
○ baby
○ real estate
○ car show
○ pool party
○ golf
○ gospel
○ speed dating
○ boat party
○ photography
○ dog
○ data science
○ business
○ kids
○ networking
○ christian
○ free

8. Search Logs
+ Machine Learning
= Automatic Tagging
John Berryman

9. Initial Approach
● Given – we have 3 tables:
○ search log
○ click log
○ event table
● Step 1: Find the most common 500 queries
● Step 2: Find all the events clicked after a user search using a common query
● Step 3: Collect the name and description of those events
● Create a training set:
○ X = input = title and body text of events
○ y = output = query string used to find them a.k.a. tags
● Train a model to predict y based on new X

10. tagging_with_searches_1.ipynb
emergency backup plan

11. Problems with this Approach
● Near synonym tags:
○ memorial day
○ memorial day weekend events
○ memorial day weekend
● Small tag vocabulary
● Each event only gets 1 or 2 tags. Sometimes 0.

12. Improved Approach
● A session may contain several queries. These queries are often related:
○ Spelling corrections
○ Word synonyms
○ Query Refinements or generalizations
● Idea:
○ Let's group statistically significant query strings together.
○ Then we can train the neural network based on the query string groups

13. query_string_clusters
and
tagging_with_searches_2
emergency backup plan
emergency backup plan

14. Things to Notice
Benefits
● Much fewer near-synonyms (bitcoin, block chain, blockchai → blockchain)
● More sample data
○ v1 model - 500 most popular queries - 33% of query traffic
○ v2 model - 2649 most popular queries collapsed down to 681 - 52% of query traffic
● Broader tags
Drawback
● Some of the clusters pull in very loosely related words
○ ai → blockchain
○ ozio → rosebar

15. Tagging-Related Applications
● Power Faceted Search
● Infer relationship between tags
● Provide organizers tag
recommendations
● Better understand supply and
demand
● Apply tags to users for better
recommendation
● Search Synonyms (e.g.
misspellings)

16. Future Work
● Better coverage
○ Currently reach 50% of our traffic with 2,500 queries.
Long tail is long! > 500K distinct queries in a month
○ Model biases towards short tail labels - everything's a "day party"
○ Can't cover searches for an event that isn't in our inventory.
● Create real pipeline
● Build out all the cool ideas on the last slide

17. Questions?
… better yet, Ideas?

18. Final notes:
● My jupyter notebooks are here:
○ First implementation
○ Query collapsing
○ Second implementation
○ Third implementation
Data Nerds
● Want to learn data science with
others? You should try Data Nerds.
● Do you like spending time around
people that love learning? Penny
University is the peer-to-peer
learning community for you!
● I just shared my talk
https://twitter.com/JnBrymn

19. This slide intentionally left blank.

20. DON'T FORGET
● Tweet the slides out just before the talk
● Open the notebooks
○ do
■ cd ~/Personal/data_science/tagging_events/
■ jupyter notebook
■ open the 3 notebooks in event_tagging_strategies
○ or just use gists: one, two, three
● Bump up the font size on the notebooks
● Remove the menus
● Clear cells