1.
Building a
Recommendation
System
November 2022
Vladimir Ageev

2.
Meet the team
Vladimir Ageev
Sr. Data Scientist
Vera Kochetkova
Data Scientist
Vadim Radchenko
Data Scientist

3.
Plan of the talk
What we are working on
How we are implementing it
How do we measure it
Why we are building it

4.
What we are building
Our product is an enterprise video management system for an
international company with thousands of employees, dozens
specialisations and different seniority levels
Townhalls/all-hands recordings
Corporate onboarding content
Technical trainings or L&D materials
Soft-skills courses
Meetup recordings
Well-being trainings
Audio podcasts
...
These users generate, view and listen
various types of video content:

5.
Why we are building it
Onboard newcomers to the company
Highlight important company or local updates
Encourage and help with professional growth
All this generated content should be exposed
to right people at the right moment and in a right context
in order to
Recommendation system is one way to achieve distribution
of content in such personalised way

6.
Why we are building it
Events
News posts Video hosting

7.
How we are building it: target
What is definition of relevant content? Depends on feedback type!
Impressions:
"how many times user seen a video but didn't click it?"
implicit
explicit
Like of the video
Like/dislike of suggestion
Addition to watch later playlist
Watch history and view ratio:
"user watched 90% of an hour long video 3 months ago"

8.
How we are building it: target
What is definition of relevant content? Depends on feedback type!
Impressions:
"how many times user seen a video but didn't click it?"
implicit
explicit
Like of the video
Like/dislike of suggestion
Addition to watch later playlist
Watch history and view ratio:
"user watched 90% of an hour long video 3 months ago"
view ratio target is not trivial,
depends on
video duration and view date
a – max duration
b – 25% quantile of durations
distribution
N – number of thresholds
p – percent watched duration
Final target is scaled

9.
How we are building it: models
Multi-stage ranking (aka hybrid recommendation system)
learning-to-rank with
WARP loss
ranking with lambdarank
objective

10.
How we are building it: models
Cold start users
Cold start content & context suggestions
Trending content: videos receiving views regularly this week
Popular content for their role, level, location:
"most popular content among Junior Business Analysts
for the past 3 months"
their role name (ML Engineer)
the last video they watched
a text post
meetup recording
Videos similar to
Helps with both user and content cold starts

11.
How we are building it: models
Cold start users
Cold start content & context suggestions
Trending content: videos receiving views regularly this week
Popular content for their role, level, location:
"most popular content among Junior Business Analysts
for the past 3 months"
their role name (ML Engineer)
the last video they watched
a text post
meetup recording
Videos similar to
Helps with both user and content cold starts
SBERT

12.
How we are building it: pipelines
Models are not enough
There is a lot of infrastructure behind
GitLab – versioning and CI
Postgress for data and features storage
Faiss for vector storage
Kafka for communication
Airflow for orchestration and scheduling
Some pipelines are scheduled,
others are triggered with appearance of new data

13.
How do we measure it
model-level
funnel
product level
How do we measure system performance?
CTR – conversion of impressions into clicks
Long CTR (mCTR) – conversion of impressions into to 80% view ratio
MAP@K – how well we are ordering recommendations
Hits@K - general relevancy of recommendations
Calculated both online and offline
Ratio of watch time - how important recsys is for content distribution
Ratio of content consumed from recsys
Retention - do users continue to watch recommended content
tools
Amplitude – events tracking
Metabase & PowerBI – dashboarding of stored metrics

14.
This is how you can build a recommender
Business goal
Target definition Orchestration
Corporate updates,
trainings and events
delivered to right
people at right
moment
Use explicit and
implicit feedback to
define and measure
relevancy
Models
Complex model for the
most active users.
Explainable
lightweight models for
treating cold start and
contextual suggestions
Infrastructure behind
operation of the
models. Based on
activity select right
tool and way of model
deployment and
inference
Quality
Measure quality offline
and online.
Select metrics at
different levels:
product, funnel, model

15.
Other projects
"We've built a Content Recommender System (news, ads) for a telecom company.
The customer already had a mobile app, but the personalization was not there.
Our team leveraged the Big Data stack (Apache Spark, Apache Ignite) to build a backend
capable to serve recommendations in real-time with low latency under high load.
It was a hybrid model built on users’ behavior, profile, and news textual content.
CTR skyrocketed. Users got personalized content."
"Recommendation system built for an international health and beauty retail group with
thousands of stores and millions of customers.
Team created multiple customer scoring models, product recommenders and promotion
recommenders. The solution was build using Apache Spark.
Horovod was used for distributed training of DL models"

16.
Feel free to reach out and connect! Opportunities in our company
Thank you!
email: vladimir_ageev@epam.com link: epa.ms/Jobs-Serbia