Alexander Konduforov: Building modern recommender systems AI & BigData Online Day 2021 Website - http://aiconf.com.ua Youtube - https://www.youtube.com/startuplviv FB - https://www.facebook.com/aiconf

1. Building a modern recommender system
Alex Konduforov
Data Science Competence Leader
AltexSoft

2. Intro to RecSys

3. RecSys are everywhere

4. Evolution
Tapestry (Xerox Lab) - emails
1992
GroupLens project (UoM) - UseNet,
MovieLens
1994
Amazon - item-based collaborative
filtering
1998
Netflix - Cinematch, Netflix Prize
2006
Spotify - uses CNNs for audio analysis
2013
Youtube
2010 - naïve recommender
2016 - Deep Neural Nets by DeepMind
2016

5. Types of recommendations
Product recommendations Content recommendations

6. • Choice overload – infinite "shelves"
• Surprising suggestions
• Higher conversion, more purchases
• Preserving customer attention
• Competitive advantage across industry
Why do we need them?

7. Main approaches
Recommender systems
Content-based filtering Collaborative filtering Hybrid systems
Memory-based
(neighborhood-based)
Model-based
(mix of content and collaborative
methods)

8. Content-based methods

9. Uses similarity between items to recommend items similar to what the user likes.
2 approaches:
1) Build vector for item
2) Find similar item vectors
(cosine, dot product, Euclidian, etc.)
1) Build vectors for user items
2) Derive a user vector from them
3) Find similar item vectors
Content-based filtering
Image source

10. User-centered modeling
Item-centered modeling
Content-based modeling
Image source

11. Collaborative-based methods

12. Uses interactions between users and items simultaneously to provide
recommendations.
Collaborative filtering
Image source

13. User-user CF
Neighborhood-based CF
Image source

14. Item-item CF
Neighborhood-based CF
Image source

15. User-user:
• Users have not many interactions
• KNN is sensitive to single interactions (high
variance)
• More personalized results (low bias)
Neighborhood-based CF
Item-item:
• Items have many users interacted with them
• KNN is less sensitive to single interactions (lower
variance)
• Less personalized results (higher bias)
• Works better for new users (not enough history)
• More likely to converge
General issues:
• KNN is time consuming, doesn't scale well
• Higher effect of "rich-get-richer" for popular items

16. Also referred as Latent factor methods
Approaches:
1. SVD
2. Matrix factorization
3. Neural Networks
Model-based CF

17. Model-based CF: Matrix Factorization
Image source
Matrix Factorization

18. Training:
• Initialize P and Q matrices with small random numbers
• Teach P and Q
• Alternating Least Squares
• Stochastic Gradient Descent
Predictions:
MF algorithm

19. MF example
Latent features are calculated via MF:
User-item score is the dot product:
Item-item similarity is the cosine similarity:

20. • MF with Biases: handling bias of some users giving higher ratings than others
• MF with Side Features: adding data to handle the cold start problem (i.e. user occupation)
• MF with Temporal Features: handling temporal changes of the data (i.e. occupation change)
• Factorization Machine: extra item features + higher order interactions
• MF with Fixture of tastes: give users several tastes
• Variational MF
MF improvements
Source article

21. Why?
• Collaborative filtering uses robust approach based on similarities between customer tastes
and can make cross-genre(category) recommendations
• Pure CF lacks information about items and suffer from sparsity in data
• Content-based filtering may find deep similarities between items and suggest novel and
surprising new items
• Only CBF can bring value from images, sounds, texts
• All modern implementations are hybrid
Hybrid
More info: https://www.researchgate.net/publication/263377228_Hybrid_Recommender_Systems_Survey_and_Experiments

22. Deep Learning for RecSys

23. Deep Learning revolution

24. • Modeling the non-linear interactions in the data
• Feature extraction directly from the content:
• Image, audio, text
• Ability to use heterogeneous data (interactions, content) in the same model
• Powerful for sequential modeling tasks (next item prediction, session-based recommendations)
• Better representation learning of users and items for CF
Deep Learning for RecSys

25. Bringing NNs to CF
Neural Collaborative Filtering Deep Factorization Machine
Several more NN-based improvements can be found here: https://towardsdatascience.com/recsys-series-part-5-neural-
matrix-factorization-for-collaborative-filtering-a0aebfe15883

26. • Prior to Deep Learning approach used Matrix Factorization
• 2 Neural Networks:
• Candidates generation: broad personalization via CF
(retrieves only couple of hundreds of videos)
• Ranking: assigns a score to each video using a rich
set of features from item and user
• Uses implicit signals (full watching is positive), not explicit
(thumbs up/down)
• Use video "age" as a feature to recommend more new
content
• Rely mostly on A/B testing results
https://research.google/pubs/pub45530/
Youtube recommendations

27. • Uses implicit customer feedback
• Back in 2014 used Weighted MF
• Used MFCC and CNNs to extract features from music –
helps with cold start and finding unpopular tracks
• Used NLP to extract features from song texts and other
textual information
• Basically uses a hybrid recommender system
https://benanne.github.io/2014/08/05/spotify-cnns.html
https://www.oreilly.com/radar/personalization-of-spotify-home-
and-tensorflow/
Spotify recommendations

28. • Autoencoder-based recommendations:
• to learn the lower-dimensional feature representations at the bottleneck layer
• to fill in the blanks of the user-item interaction matrix directly in the reconstruction layer
• CNN-based recommendations:
• to extract features from images
• to extract features from audio and video
• to extract features from texts
• RNN-based recommendations
• to extract sequential patterns in session-based tasks
• and many others: https://towardsdatascience.com/recommendation-system-series-part-2-the-10-
categories-of-deep-recommendation-systems-that-189d60287b58
Other DL approaches

29. Metrics:
• When predicting rating: MSE, RMSE, etc.
• When predicting binary output: Accuracy, Precision, Recall, F1
• When predicting top N: MAP@N, MAR@N
• Coverage: % of items which are recommended (average CF predicts ~8-10%)
Real-world testing:
• CTR or CR (product, ads, etc.)
• Avg Time spent or Customer retention (content)
• A/B testing
• Serendipity: pleasant surprise, unintended discovery
Evaluation

30. • Novelty
• Diversity
• Serendipity
• Interpretability
• Adaptation
Qualities of a good recommender

31. • https://towardsdatascience.com/introduction-to-recommender-systems-6c66cf15ada
• https://towardsdatascience.com/recommendation-system-series-part-1-an-executive-guide-to-
building-recommendation-system-608f83e2630a
• https://medium.com/libreai/a-glimpse-into-deep-learning-for-recommender-systems-
d66ae0681775
• ADD: https://towardsdatascience.com/modern-recommender-systems-a0c727609aa8
Materials

32. Alex Konduforov
alexander.konduforov@gmail.com
@alex_konduforov
Thank you!