1.
A Tensorflow
Recommending System
for News
Fabricio Vargas Matos

2.
Manhattan, NYTV Stations
Local and National News

3.
Article’s page: recommendations
for continuous scroll section
Recommended articles

4.
Agenda
1.Recency and cold-start problem
2.Data acquisition
3.Matrix factorization
4.Tensorflow implementation
5.Hybrid Model: NLP and feature engineering
6.Hybrid Model: Hybrid matrix factorization
7.Conclusions

5.
Cold-start problem
Existent
Items
New
Items
Existent Users New Users

6.
Cold-start solution
Existent
Items
New
Items
Existent Users New Users
Not personalized!
Curated by Editors
+
Highly viewed

7.
Cold-start solution
Existent
Items
New
Items
Existent Users New Users
Not personalized!
Curated by Editors
+
Highly viewed
Hybrid
Matrix
Factorization

8.
Data Acquisition
Page views with
user’s time on page
Google Analytics Google BigQuery CMS
Content corpus: title,
body, timestamp,
meta-data (sections,
tags, etc.)
Contents
TFRecord/CSV files

9.
"Users x Items" Sparsity
Dataset Sparsity
MovieLens (movies) 98.61%
Netflix (movies) 98.82%
TV Stations (news) 99.94%
Yahoo! KDD (music) 99.96%

10.
Matrix Factorization

11.
VU
Latent Factors Model
R
Items
Users
≈
Latent
factors
Latent
factors
Items
xuserbias
item bias
i
j
i
j
R[i,j] ≈ U[i] x V[j]

12.
TF code: factorization op
(…)

13.
TF code: train op

14.
Initial Results
• Training time ≈ 15min (Kubernetes cluster)
• TimeOnPage Prediction Error (RMSE) ≈ 125 sec
• Qualitative recommendation tests with chosen
‘personas’ revealed poor personalization

15.
Hybrid Matrix
Factorization Model

16.
Natural Language
Processing
Concatenate content data
(title, body, sections, tags, …)
Remove stop words, symbols
and HTML tags
Train word2vec Neural Network
Combine all word-vectors of
each article into one (doc2vec)
CMS
articles
doc2vec
contents

17.
Contents Data
Visualization

18.
Entertainment
National News
Health
Sports
Local News

19.
Features Engineering
NLP (doc2vec)
items clustering (k-means)
embed items:
similarity to each cluster centroid
embed users:
viewed contents combined
CMS
articles
k-dimension
items/users
embeddings
Google
Cloud
Storage

20.
Items Parallel coordinates: 40 features/clusters

21.
Feature #1: Similarity to
cluster #1

22.
Feature #39

23.
Who are they?
Magenta contents (health) with high
values for feature #1 (economy)?

24.
Content/User Embeddings
+
Matrix Factorization

25.
VU
Matrix Factorization
R
Items
Users
≈
Latent
factors
Latent
factors
Items
xuserbias
item bias
i
j
i
j
R[i,j] ≈ U[i] x V[j]

26.
Hybrid Matrix Factorization
• R ≈ U* x V*
where:
• U* = UUsersxKClusters x AKClustersxLatent_factors
• V* = BLatent_factorsxKClusters x VKClustersxItems
*Only A and B are variables to be trained. U and V are constants.

27.
TF code: factorization
Now:

28.
Results
• Training time ≈ 20min (Kubernetes cluster)
• TimeOnPage Prediction Error (RMSE) ≈ 100 sec
(20% better)
• Qualitative recommendation tests with chosen
‘personas’ revealed very good personalization
• R&D Project - Not yet publicly available

29.
Let’s talk online
fabriciovargasmatos@
Fabricio Vargas Matos