Blog
The latest news from Google AI
Wide & Deep Learning: Better Together with TensorFlow
Wednesday, June 29, 2016
Posted by Heng-Tze Cheng, Senior Software Engineer, Google Research
The human brain is a sophisticated learning machine, forming rules by memorizing everyday events (“sparrows can fly” and “pigeons can fly”) and generalizing those learnings to apply to things we haven't seen before (“animals with wings can fly”). Perhaps more powerfully, memorization also allows us to further refine our generalized rules with exceptions (“penguins can't fly”). As we were exploring how to advance machine intelligence, we asked ourselves the question—can we teach computers to learn like humans do, by combining the power of memorization and generalization?
It's not an easy question to answer, but by jointly training a wide linear model (for memorization) alongside a deep neural network (for generalization), one can combine the strengths of both to bring us one step closer. At Google, we call it
Wide & Deep Learning
. It's useful for generic large-scale regression and classification problems with sparse inputs (
categorical features
with a large number of possible feature values), such as recommender systems, search, and ranking problems.
Today we’re open-sourcing our implementation of
Wide & Deep Learning
so that you can easily train a model yourself. Please check out the TensorFlow tutorials on
Linear Models
and
Wide & Deep Learning
, as well as our
research paper
to learn more.
How Wide & Deep Learning works.
Let's say one day you wake up with an idea for a new app called
FoodIO
*
. A user of the app just needs to say out loud what kind of food he/she is craving for (the
query
). The app magically predicts the dish that the user will like best, and the dish gets delivered to the user's front door (the
item
). Your key metric is consumption rate—if a dish was eaten by the user, the score is 1; otherwise it's 0 (the
label
).
You come up with some simple rules to start, like returning the items that match the most characters in the query, and you release the first version of FoodIO. Unfortunately, you find that the consumption rate is pretty low because the matches are too crude to be really useful (people shouting “fried chicken” end up getting “chicken fried rice”), so you decide to add machine learning to learn from the data.
The Wide model.
In the 2nd version, you want to memorize what items work the best for each query. So, you train a linear model in TensorFlow with a
wide
set of cross-product feature transformations to capture how the co-occurrence of a query-item feature pair correlates with the target label (whether or not an item is consumed). The model predicts the probability of consumption P(consumption | query, item) for each item, and FoodIO delivers the top item with the highest predicted consumption rate. For example, the model learns that feature
AND(query="fried chicken", item="chicken and waffles")
is a huge win, while
AND(query="fried chicken", item="chicken fried rice")
doesn't get as much love even though the character match is higher. In other words, FoodIO 2.0 does a pretty good job
memorizing
what users like, and it starts to get more traction.
The Deep model.
Later on you discover that many users are saying that they're tired of the recommendations. They're eager to discover similar but different cuisines with a “surprise me” state of mind. So you brush up on your TensorFlow toolkit again and train a
deep
feed-forward neural network for FoodIO 3.0. With your deep model, you're learning lower-dimensional dense representations (usually called embedding vectors) for every query and item. With that, FoodIO is able to
generalize
by matching items to queries that are close to each other in the embedding space. For example, you find that people who asked for “fried chicken” often don't mind having “burgers” as well.
Combining Wide and Deep models.
However, you discover that the deep neural network sometimes generalizes too much and recommends irrelevant dishes. You dig into the historic traffic, and find that there are actually two distinct types of query-item relationships in the data.
The first type of queries is very targeted. People shouting very specific items like “iced decaf latte with nonfat milk” really mean it. Just because it's pretty close to “hot latte with whole milk” in the embedding space doesn't mean it's an acceptable alternative. And there are millions of these rules where the transitivity of embeddings may actually do more harm than good. On the other hand, queries that are more exploratory like “seafood” or “italian food” may be open to more generalization and discovering a diverse set of related items. Having realized these, you have an epiphany: Why do I have to choose either wide or deep models? Why not both?
Finally, you build FoodIO 4.0 with Wide & Deep Learning in TensorFlow. As shown in the graph above, the sparse features like
query="fried chicken"
and
item="chicken fried rice"
are used in both the wide part (left) and the deep part (right) of the model. During training, the prediction errors are backpropagated to both sides to train the model parameters. The cross-feature transformation in the wide model component can memorize all those sparse, specific rules, while the deep model component can generalize to similar items via embeddings.
Wider. Deeper. Together.
We're excited to share the TensorFlow API and implementation of Wide & Deep Learning with you, so you can try out your ideas with it and share your findings with everyone else. To get started, check out the
code on GitHub
and our TensorFlow tutorials on
Linear Models
and
Wide & Deep Learning
.
Acknowledgement
Bringing Wide & Deep from idea, research to implementation has been a huge team effort. We'd to like to thank all the people who have contributed to the project or have given us advice, including: Heng-Tze Cheng, Mustafa Ispir, Zakaria Haque, Lichan Hong, Rohan Anil, Denis Baylor, Vihan Jain, Salem Haykal, Robson Araujo, Xiaobing Liu, Yonghui Wu, Thomas Strohmann, Tal Shaked, Jeremiah Harmsen, Greg Corrado, Glen Anderson, D. Sculley, Tushar Chandra, Ed Chi, Rajat Monga, Rob von Behren, Jarek Wilkiewicz, Christine Robson, Illia Polosukhin, Martin Wicke, Gus Katsiapis, Alexandre Passos, Olivier Chapelle, Levent Koc, Akshay Naresh Modi, Wei Chai, Hrishi Aradhye, Othar Hansson, Xinran He, Martin Zinkevich, Joe Toth, Anton Rusanov, Hemal Shah, Petros Mol, Frank Li, Yutaka Suematsu, Sameer Ahuja, Eugene Brevdo, Philip Tucker, Shanqing Cai, Kester Tong, and more.
*
For illustration only. FoodIO is not a real app.
↩
Labels
2018
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2019
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
