This document discusses recommendation systems and how to develop them. It begins by introducing the speaker and an overview of the topics to be covered. It then explains what recommendation systems are and different types including search, content-based, and collaborative filtering. It discusses drawbacks like cold start problems and sparsity and ways to address them. The document concludes with tips for refining recommendation models like normalization, capturing trends, and temporal factors.

1. Demystifying
Recommendation
Systems

2. About Rumman
•Senior Data Scientist and Instructor at Metis
•Practicing Data Scientist
• Find me on twitter @ruchowdh
• Visit my website at rummanchowdhury.com
• Check out my jobs page
• …and my blog

3. About Metis
• Data Science Bootcamp
• Part of Kaplan
• Accredited by ACCET
• 12-weeks, full-time including 60 hours of
online pre-work
• Evening and weekend training courses
• Third party financing options
• $3,000 scholarship for women,
underrepresented minority groups, and
veterans or members of the U.S. military

4. Overview
• What is a recommendation engine?
• What are the types of recommendation
systems?
• What are the drawbacks of the most
common recommendation engines and how
do I deal with them?
• How do I fine-tune my model?

5. What are recommendation
systems?

6. What are recommendation systems?
Automated systems that seek to suggest whether a
given item (product, event, movie, song, etc) will be
desirable to a user.
Or, more data science-y: predict what a user’s
review will be for items that they have not
reviewed

7. Where does a recommendation system lie in the
space of data science and analytics?
• Descriptive
• Average, percents, etc
• Explains post-event or during
• Predictive
• Uses modeling of past behavior to make
predictions about the future
• Prescriptive
• Informed decision of how actions
should be taken based on data

8. How do I pick the best kind of recommender system
for my data?
• What is your existing data?
• How quickly does your inventory change?
• How much information can you get on a
user? (explicit and implicit)
• Does your model need to scale well?

9. What are the kinds of
recommendation systems?

10. What are the kinds of recommender systems?
• Search (knowledge-based)
• Pros: items will be close matches to
expressed needs, no cold-start issues
• Cons: Static, manual tagging, will not
work well with very similar inventories
or rapidly changing inventories
• Example: Amazon’s basic search

11. What are the kinds of recommender systems?
• Content-based
• Items are mapped based on characteristics into
an item-feature space, and recommendations
are based on specified characteristics
• Pros: Easier comparison between items
• Cons: Cold start problem, need good content
descriptions, need item ratings
•Example: Search for ‘ai’ vs ‘AI’,
‘mit’ vs ‘MIT’

12. What are the kinds of recommender systems?
• Collaborative filtering: based on user and
item similarities
• Pros: can provide less-obvious matches
• Cons: cold-start problem for new users and
new items, requires a feedback rating

13. Limitations, or, Ask yourself, do you really need a
recommendation engine?
• Recommendation systems have to update immediately.
• You have to have a sufficiently inexpensive
model and have the bandwidth to return results
fast.
• You have more information than you think:
• existing item popularity
• geography based in ip address
• cookies

14. How does Content-Based recommendation work?
• Users and items are represented by vectors
in a feature space
• Approaches:
• Map users and items to the same
feature space, compute distance
between a user and an item.

15. Example: Content-Based Recommendation
Features = (big box office, aimed at kids, famous actors)
Items (movies):
Finding Nemo = (5, 5, 2)
Mission Impossible = (3, -5, 5)
Jiro Dreams of Sushi = (-4, -5, -5)
Predicted ratings*:
(-3*5 + 2*5 + 2*2) = -9
(-3*3 - 2*5 - 2*5) = -29
(3*4 - 2*5 + 2*5) = +12
* Ratings for user with a described
preference of (-3, 2, 2) for these features

16. How does Content Based Recommendation work?
• Another option is to create features from
user+item pairs and use an algorithm
(classifier?) to predict like/dislike
•Each user/item pair has a labeled outcome,
such as purchased/not purchased. You can
train a model to predict purchase behavior.

17. How does Collaborative Filtering work?
• Collaborative filtering refers to a family of
methods for predicting ratings where instead of
thinking about users and items in terms of a
feature space, we are only interested in the
existing user-item ratings themselves.
•In this case, our dataset is a ratings matrix whose
columns correspond to items, and whose rows
correspond to users.

18. Example: Netflix movie recommendations

19. How does collaborative filtering work?
• Method 1: Item-based CF, a.k.a. neighborhood
methods or memory-based CF
• Ratings data are used to create an item-item
similarity matrix.
• Recommendations are made based on the items
most similar to those a user has already rated
highly.
•This method does not scale well.
• Why? You need a fully populated matrix of
item-item similarity. This doesn’t work well
if you have a lot of items or if your items
change a lot.

20. How does CF work?
• Method 2: Model-based CF use matrix
decomposition via singular value
decomposition (SVD) to reduce
dimensionality and extract latent variables.
• We express users and items in terms of
these variables.

21. Why is model-based CF preferred?
• Scalable, flexible, accurate, domain
independent, and requires no explicit
information.

22. What are the drawbacks, and
how can I address them?

23. Let’s discuss the drawbacks
• Cold-start problem!
• Data is typically very sparse
•Need granularity in your data

24. Drawback: Cold Start problem
• Build an initial profile based on implicit
data, evolve based on explicit feedback as it
comes.
• Sometimes called a ‘hybrid’ filtering
method, you can use content-based
information to ease cold-start and data
sparsity problems.

25. Drawback: Sparsity of Data
• Famous Netflix prize dataset, ~ 99% of
possible ratings were missing.
• Data is skewed and sparse
• or, most people don’t rate a lot and
most items aren’t rated
• those that are often are rated
constantly

26. Drawback: Granularity of data
• Traditional model-based CF works well for
non-binary data (ie, a 5 star rating). Doesn’t
work well for binary (ie, click/not click,
purchased/did not purchase)
• You will need to tweak your
measurements of item similarities

27. Quick overview of measurement
• Non-binary rating:
• Pearson correlation coefficient
• Euclidean distance
• Manhattan distance
• Binary ratings:
• Jaccard similarity
• Cosine similarity

28. How do I refine my
model?

29. Normalization
• Some items are significantly higher rated
(ie, blockbuster movies, Oscar winners)
• Some users are lower (or higher) raters
from the norm
• Ratings can change over time

30. Normalization
• Need to offset per user
• Need to offset per item
•Ex: Mean rating across all users for item x is
some value. How does it differ from the mean
rating across all items? How does my rating
differ from the mean rating of that item?

31. Capturing data trends
• Rating distributions:
• ratings aren’t random, they follow a
distribution - model this distribution
• Feature importance: You can regress on your
feature vectors to get an understanding of what
values impact ratings
• Feature generation: Characterize your users and
create one-hot features (this can save a lot of time,
and help with cold-start problems)

32. Temporal factors
• There can be an upward trend of ratings
over time
• Seasonal shifts due to holidays, awards, etc
• Anchoring (ie, an item based on a previous
iteration or version of that item)

33. Conclusions
• Think about your data, your capabilities,
and your needs prior to creating a
recommendation system
• Consider the pros and cons of each type
• Refine your model thoughtfully

34. Questions?
www.rummanchowdhury.com
@ruchowdh