The document discusses recommender systems and how deep learning techniques can be applied. It provides an overview of traditional recommender approaches like content-based filtering, collaborative filtering, and hybrid systems. It then outlines how deep learning methods such as neural networks, item embeddings, deep matrix factorization, and session-based models can be used to power recommender systems. The document emphasizes starting simple, experimenting with deep learning, knowing the optimization goal, having strong evaluation practices, and bringing in experts to help build and scale systems.

1. Max Pumperla
Oslo, September 27th 2017

2. PERSONALISATION AND
RECOMMENDATION
From algorithm to production

3. AGENDA
1. About Skymind
2. Fundamentals
2.1. What to recommend?
2.2. What is a recommendation?
2.3. What data can we use?
3. Types of recommenders
3.1. Content-based
recommendation
3.2. Collaborative filtering
3.3. Hybrid approaches
4. Evaluation
5. Enter deep learning
5.1. Neural networks
5.2. Item embeddings
5.3. Deep matrix factorization
5.4. Session-based recos
5.5. Combining approaches
6. Summary

4. 1. SKYMIND

5. Founded 2014
Clients 14 Enterprises
3,500 GH Forks, 7,200 Stars
300,000+ DL4J downloads/mo.
Team ~35; mostly engineers; 7 PhDs
COMPANY OVERVIEW

6. Deeplearning4j
Build, train, and
deploy neural
networks on JVM
ND4J
High performance
linear algebra CPU
and GPU libraries
DEEP LEARNING AT SKYMIND

7. 2. FUNDAMENTALS

8. WHAT TO RECOMMEND?

9. WHAT TO RECOMMEND?

10. WHAT IS A
RECOMMENDATION?
● Compute a predictive model from data
● Calculate predictions and rank them
● Suggest items (best, top N)
● Respect business rules and other constraints
● Optimize for metric of choice

11. WHAT DATA CAN WE USE?
INPUT & LABELS
Ratings:
(user_id, item_id, rating)
Events (shopping cart,
wish list, page view):
(user_id, item_id)

12. WHAT DATA CAN WE USE?
● Implicit vs. explicit
● Beware: absence of evidence vs evidence of
absence
● What do we really want to model for?

13. 3. RECOMMENDER
SYSTEMS

14. CONTENT-BASED RECOS
● Explicitly model features from
content and user data
● Might use meta-data
● Build feature vector representation

15. EXAMPLE: TF-IDF
● Number of documents N
● Term frequency TF: count
terms in document
● Document frequency DF:
count how of often term is in
corpus
● TF-IDF = TF / ( DF / N )
Vector representation
of content

16. EXAMPLE: ITEM-ITEM
BASED RECO
● Use TF-IDF to vectorize items
● Build user profile vector by
aggregating relevant items
● Measure how close an item is to
a user (e.g. cosine similarity)
User
Item A
Item B

17. COLLABORATIVE FILTERING
● Implicitly model features from user
behaviour and interaction data
● “Users who liked A also liked …”
Users
Users
Items
Items

18. EXAMPLE: USER-USER
BASED RECO
● How close are my ratings from
yours on average?
● e.g. Pearson correlation, k-NN etc.
● Recommend items from users
close to you
Users
Users

19. EXAMPLE: MATRIX
FACTORIZATION
● Decompose User-Item matrix
into User and Item matrices
● Different ways to do so
● Creates features for both
● Use this to predict ratings by
matrix multiplication
Users
Items
Users
Items

20. HYBRID SYSTEMS
● Combine content-based and collaborative approaches
● Can help overcome weaknesses of both
● Many ways to do so

21. 4. EVALUATION

22. IS IT WORTH WINNING THE
NETFLIX PRIZE?
● Accuracy is just one metric
● Precision vs recall
● Diversity vs top sellers
● Serendipity

23. HOW TO TEST?
● Use hold-out sets:
○ remove item and see if it gets
recommended
○ does not generalize very well
● Use A/B testing:
○ more difficult to do right
○ holy grail of reco systems

24. 5. DEEP LEARNING

25. NEURAL NETWORKS
● Map inputs to outputs through layers of
neurons.
● Brain-inspired? Birds vs airplanes
● Flexible & powerful
● Many successes

26. DEEP LEARNING
● Powered by data, compute & theory
● Representation learning: remove
feature engineering from the equation
● Can model and combine a variety of
data types
● Highly applicable to reco systems

27. ITEM EMBEDDINGS
Learnable vectorization
User
Item A
Item B

28. ITEM EMBEDDINGS
● Structure of documents can be learned
instead of imposed
● Very successful applications of this,
especially with natural language
● Might discover unforeseen patterns

29. DEEP MATRIX FACTORIZATION
0 … 0 1 0 … 0
Users
0 … 0 1 0 … 0
Products
Embeddings
Merge
Dense
Dense
Dense
Prediction

30. DEEP MATRIX FACTORIZATION
● Collaborative filtering with Deep learning
● Learns features for users and items
automatically
● Can predict ratings and other outcomes

31. DEEP SESSION-BASED RECOS
● Utilize sequentiality of events
● Recurrent neural networks for modeling
time dependencies
● Challenging, but can prevent pathologies

32. COMBINING APPROACHES
● Can build complex models from components
● Integrate meta-data, learned features etc.
● Anna Karenina principle

33. 6. SUMMARY

34. TAKE-AWAYS
● Start simple
● Experiment with Deep Learning
● Know what you want to optimize for
● Have solid A/B testing in place
● Bring experts in to help build, deploy and
scale

35. max@skymind.io
twitter: maxpumperla
github: maxpumperla

36. DL4J MODEL IMPORT
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