Slides for the presentation on "Session-based recommender systems" at Qvik.

1. Session-Based Recommender Systems
FREDERICK AYALA GÓMEZ
PHD STUDENT IN COMPUTER SCIENCE
@FREDAYALA FREDERICKAYALA
4/20/2017 @FREDAYALA LINKEDIN.COM/IN/FREDERICKAYALA 1
Presented at

2. Agenda
Why bother?
What are Recommender Systems?
How to build recommendations?
Hands-on GRU4Rec
Conclusions
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3. Are we good at
choosing among
multiple
options?
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happiness
choices
Having too many choices is stressful
- Takes time
- Increase uncertainty

4. Web and SoMe
as a channel to
consume
content,
products and
services.
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Taken from https://www.wired.com/2004/10/tail/

5. What are
recommender
systems?
Not so formal definition
Finding
relevant things
for a user based
on some kind
of feedback
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Products
Content
Services
Offer
User
interests
What is
Relevant

6. “Recommendations account for about 60% of all video
clicks from the home page” <- 2010James Davidson, Benjamin Liebald, Junning Liu, Palash Nandy, Taylor Van Vleet, Ullas Gargi, Sujoy Gupta, Yu He, Mike Lambert, Blake Livingston, and Dasarathi Sampath.
2010. The YouTube video recommendation system. In Proceedings of the fourth ACM conference on Recommender systems (RecSys '10). ACM, New York,
NY, USA, 293-296. DOI=http://dx.doi.org/10.1145/1864708.1864770
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“Recommendations account for about 60% of all video clicks
from the home page.”

7. What are recommender systems?
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8. Types of
Feedback
Explicit Feedback
“Asking the user”
Ratings
Like/Dislike
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Implicit Feedback is
more common in
practice
Implicit
Feedback
“Observe the
user behaviors”
Clicks
Previously
seen items

9. How to build recommender systems?
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Two books about Recommender Systems

10. Matrix Factorization
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Represent the users/items feedback as a matrix. Then, find two lower dimension matrixes that factorize it
R
Usually very
sparse since
users consume
few items
ItemsUsers
≈ P
Q
Users
Itemsf
f
Y. Koren, R. Bell and C. Volinsky, "Matrix
Factorization Techniques for Recommender
Systems," in Computer, vol. 42, no. 8, pp. 30-37,
Aug. 2009.
doi: 10.1109/MC.2009.263
Minimize the squared error function
This is done usually by Stochastic Gradient Descent or Alternating Least Squares
For binary feedback use iALS
The predicted score for each user-item is given by
Works very well for personalization (e.g. newsletters)

11. No user profile
…
Old feedback
could be irrelevant
…
~84% of our
recommendations
are item-to-item
- DomonkosTikk,CEO@GravityR&D
Lessons learnt at building
recommendation services at industry
scale,
https://www.slideshare.net/domonkostikk/lessons-
learnt-at-building-recommendation-services-at-
industry-scale
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Online Shopping Blogs
Browsing AnonymouslyNews/Expiring Content

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Linden et al. 2003. “Amazon.com Recommendations: Item-to-Item Collaborative Filtering” IEEE Internet Computing
Davidson et al. 2010. “The YouTube video recommendation system”. ACM RecSys '10. BCN, Spain.
The next item is recommended based on the similarity between the actual item and other items.
The similarity is computed by using the two item-session vectors:
With this similarities we can find the k Nearest Neighbors of item i.
Usually a very strong baseline.
1 2 3 . . . . i . . . j . . . . . m
1 1 1
2
. 1 1
. 1 1
. 1 1
n 1
Items
Sessions
Item-KNN
“people who viewed this item also viewed…”

13. Item-to-Item Recommendations
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User Item
User 1 A
User 1 B
User 1 C
User 2 B
User 2 C
User 2 D
User 3 B
User 3 C
User 3 A
Item i Item j Count
B C 3
A B 2
C A 1
C D 1

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Where:
i: Current item
j: Next item
k: Other items in the dataset
yi: Latent Factor for item i
yj: Latent Factor for item j
yk: Latent Factor for item k
bj: Bias item j
bj: Bias item k
N. Koenigstein, Y. Koren. "Towards scalable and accurate item-oriented recommendations". ACM RecSys '13. NY, USA.
Learn model parameters that maximize the log-likelihood of
the training set:
Using Stochastic Gradient Descent and Importance
Sampling to reduce the complexity of the training
After concatenating the biases to the latent factors, the best next item j is the closest vector to the item i
Not the best baseline for top-K evaluations
Euclidean Item Recommender

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Recurrent
Neural Networks
Train a model with a
sequence that predicts
a higher probability for
the next element.
Also called
sequence-to-sequence
models
We learned the alphabet in this direction
Telling it backwards is harder

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Recurrent Neural Networks
Taken from: http://karpathy.github.io/2015/05/21/rnn-effectiveness/
H
E
L
O
H
E
L
O
Input and
outputs are
one-hot-
encoded
Backpropagate to fix
errors (i.e. move the
hidden layers weights a
small amount in the
direction of the loss
function gradient)
nok
nok ok
ok

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19. GRU4RecB. Hidasi, et al. 2016 “Session-based recommendations with recurrent neural networks”. ICLR 16’, San Juan, PR
Taken from the authors ICLR poster http://www.hidasi.eu/content/gru4rec_iclr16_poster.pdf
Optimize directly to the ranking using BPR or TOP1
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20. GRU4Rec - PerformanceB. Hidasi, et al. 2016 “Session-based recommendations with recurrent neural networks”. ICLR 16’, San Juan, PR
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𝑅𝑒𝑐𝑎𝑙𝑙@20 =
# 𝑅𝑒𝑐𝑜𝑚𝑚𝑒𝑛𝑑𝑎𝑡𝑖𝑜𝑛𝑠 𝑤𝑖𝑡ℎ 𝑡ℎ𝑒 𝑛𝑒𝑥𝑡 𝑑𝑒𝑠𝑖𝑟𝑒𝑑 𝑖𝑡𝑒𝑚 𝑖𝑛 𝑡ℎ𝑒 𝑇𝑜𝑝 20 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛𝑠
𝑇𝑜𝑡𝑎𝑙 # 𝑜𝑓 𝑟𝑒𝑐𝑜𝑚𝑚𝑒𝑛𝑑𝑎𝑡𝑖𝑜𝑛𝑠
Collection of sessions – item click events

21. Hands-on GRU4REC
Jupyter Notebook at
https://github.com/frederickayala/session-based-recsys
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22. Conclusions
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Frederick Ayala Gómez
PhD Student in Computer Science
@fredayala frederickayala
Session-Based Recommender Systems are very common in real life
GRU4Rec code is open and GPU accelerated with Theano
Thanks!
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