Ten Lessons Learned from Building (real-life impactful) Machine Learning Systems
(This is a blogpost version of a talk I gave at MLConf SF 11/14/2014. See below for original video and slides)
Posts by Tag
- recommender systems 9
- machine learning 3
- research 2
- explicit feedback 2
- netflix 2
- big data 2
- models 2
- conferences 1
- presentation skills 1
- game dynamics 1
- quora 1
- Social Networks 1
- trust 1
- agile 1
- extreme Programming 1
- mamagement 1
- scrum 1
- noise 1
- error 1
- stats 1
- explicit 1
- implicit 1
- music recommenders 1
- user modeling 1
- interaction 1
- presentation data 1
- conference 1
- recsys11 1
- netflix prize 1
- recsys 1
- software 1
recommender systems
Introduction to Recommender Systems: A 4-hour lecture
A couple of weeks ago, I gave a 4 hour lecture on Recommender Systems at the 2014 Machine Learning Summer School at CMU. The school was organized by Alex Smo...
Beyond the 5 Stars Round up
Last week, I published a post on the Netflix tech blog. The post, entitled “Netflix Recommendations: Beyond the 5 stars” describes how recommendations have e...
Recsys 2011 – Notes and Pointers
I found Recsys this year of very high quality in general. There were many good papers and presentations. The Industry track was also very high-quality, with...
The Recommender Problem & the Presentation Context
In the traditional formulation of the “Recommender Problem”, we have pairs of items and users and user feedback values for very few of those dyads. The probl...
Joining Netflix
Three weeks ago, I started to work for Netflix. Everything has moved so fast with so many things to do and learn that it seems like I have already been here ...
Walk the Talk: On the Combination of Implicit and Explicit Feedback
Last week, Denis Parra presented our paper entitled “Walk the Talk: Analyzing the Relation between Implicit and Explicit Feedback for Preference Elicitation”...
Recommender Systems: We’re doing it (all) wrong
A few days back, there was an interesting post by Judy Robertson in the Communications of the ACM blog. The post, entitled “Stats: We’re doing it wrong“, bui...
The Science and the Magic of User Feedback
That was the main title of a series of talks I gave in different labs and companies during my recent California tour. In this presentation, I talked about ma...
machine learning
Ten Lessons Learned from Building (real-life impactful) Machine Learning Systems
(This is a blogpost version of a talk I gave at MLConf SF 11/14/2014. See below for original video and slides)
Introduction to Recommender Systems: A 4-hour lecture
A couple of weeks ago, I gave a 4 hour lecture on Recommender Systems at the 2014 Machine Learning Summer School at CMU. The school was organized by Alex Smo...
More data or better models?
The discussion of whether it is better to focus on building better algorithms or getting more data is by no means new. But, it is really catching on lately. ...
research
Managing Research the Agile Way
I have discussed previously on this blog about how well the Scientific Method adapts to Agile approaches. These ideas also took me to an unfinished effort to...
Did you prepare your talk?
I don’t consider myself to be a great presenter. As a matter of fact, every time I finish a presentation, I find myself thinking about how many things I scre...
explicit feedback
Walk the Talk: On the Combination of Implicit and Explicit Feedback
Last week, Denis Parra presented our paper entitled “Walk the Talk: Analyzing the Relation between Implicit and Explicit Feedback for Preference Elicitation”...
The Science and the Magic of User Feedback
That was the main title of a series of talks I gave in different labs and companies during my recent California tour. In this presentation, I talked about ma...
netflix
Beyond the 5 Stars Round up
Last week, I published a post on the Netflix tech blog. The post, entitled “Netflix Recommendations: Beyond the 5 stars” describes how recommendations have e...
Joining Netflix
Three weeks ago, I started to work for Netflix. Everything has moved so fast with so many things to do and learn that it seems like I have already been here ...
big data
Ten Lessons Learned from Building (real-life impactful) Machine Learning Systems
(This is a blogpost version of a talk I gave at MLConf SF 11/14/2014. See below for original video and slides)
More data or better models?
The discussion of whether it is better to focus on building better algorithms or getting more data is by no means new. But, it is really catching on lately. ...
models
Ten Lessons Learned from Building (real-life impactful) Machine Learning Systems
(This is a blogpost version of a talk I gave at MLConf SF 11/14/2014. See below for original video and slides)
More data or better models?
The discussion of whether it is better to focus on building better algorithms or getting more data is by no means new. But, it is really catching on lately. ...
conferences
Did you prepare your talk?
I don’t consider myself to be a great presenter. As a matter of fact, every time I finish a presentation, I find myself thinking about how many things I scre...
presentation skills
Did you prepare your talk?
I don’t consider myself to be a great presenter. As a matter of fact, every time I finish a presentation, I find myself thinking about how many things I scre...
game dynamics
On Trust Networks and Gamification. Or How Quora can overcome its Hype and embrace long-term Success
If you are reading this blog I am pretty sure that you know quite a lot about Quora by now. If not, you should sign on and try it a bit before you continue r...
quora
On Trust Networks and Gamification. Or How Quora can overcome its Hype and embrace long-term Success
If you are reading this blog I am pretty sure that you know quite a lot about Quora by now. If not, you should sign on and try it a bit before you continue r...
trust
On Trust Networks and Gamification. Or How Quora can overcome its Hype and embrace long-term Success
If you are reading this blog I am pretty sure that you know quite a lot about Quora by now. If not, you should sign on and try it a bit before you continue r...
agile
Managing Research the Agile Way
I have discussed previously on this blog about how well the Scientific Method adapts to Agile approaches. These ideas also took me to an unfinished effort to...
extreme Programming
Managing Research the Agile Way
I have discussed previously on this blog about how well the Scientific Method adapts to Agile approaches. These ideas also took me to an unfinished effort to...
mamagement
Managing Research the Agile Way
I have discussed previously on this blog about how well the Scientific Method adapts to Agile approaches. These ideas also took me to an unfinished effort to...
scrum
Managing Research the Agile Way
I have discussed previously on this blog about how well the Scientific Method adapts to Agile approaches. These ideas also took me to an unfinished effort to...
noise
The Science and the Magic of User Feedback
That was the main title of a series of talks I gave in different labs and companies during my recent California tour. In this presentation, I talked about ma...
error
Recommender Systems: We’re doing it (all) wrong
A few days back, there was an interesting post by Judy Robertson in the Communications of the ACM blog. The post, entitled “Stats: We’re doing it wrong“, bui...
stats
Recommender Systems: We’re doing it (all) wrong
A few days back, there was an interesting post by Judy Robertson in the Communications of the ACM blog. The post, entitled “Stats: We’re doing it wrong“, bui...
explicit
Walk the Talk: On the Combination of Implicit and Explicit Feedback
Last week, Denis Parra presented our paper entitled “Walk the Talk: Analyzing the Relation between Implicit and Explicit Feedback for Preference Elicitation”...
implicit
Walk the Talk: On the Combination of Implicit and Explicit Feedback
Last week, Denis Parra presented our paper entitled “Walk the Talk: Analyzing the Relation between Implicit and Explicit Feedback for Preference Elicitation”...
music recommenders
Walk the Talk: On the Combination of Implicit and Explicit Feedback
Last week, Denis Parra presented our paper entitled “Walk the Talk: Analyzing the Relation between Implicit and Explicit Feedback for Preference Elicitation”...
user modeling
Walk the Talk: On the Combination of Implicit and Explicit Feedback
Last week, Denis Parra presented our paper entitled “Walk the Talk: Analyzing the Relation between Implicit and Explicit Feedback for Preference Elicitation”...
interaction
The Recommender Problem & the Presentation Context
In the traditional formulation of the “Recommender Problem”, we have pairs of items and users and user feedback values for very few of those dyads. The probl...
presentation data
The Recommender Problem & the Presentation Context
In the traditional formulation of the “Recommender Problem”, we have pairs of items and users and user feedback values for very few of those dyads. The probl...
conference
Recsys 2011 – Notes and Pointers
I found Recsys this year of very high quality in general. There were many good papers and presentations. The Industry track was also very high-quality, with...
recsys11
Recsys 2011 – Notes and Pointers
I found Recsys this year of very high quality in general. There were many good papers and presentations. The Industry track was also very high-quality, with...
netflix prize
Beyond the 5 Stars Round up
Last week, I published a post on the Netflix tech blog. The post, entitled “Netflix Recommendations: Beyond the 5 stars” describes how recommendations have e...
recsys
Introduction to Recommender Systems: A 4-hour lecture
A couple of weeks ago, I gave a 4 hour lecture on Recommender Systems at the 2014 Machine Learning Summer School at CMU. The school was organized by Alex Smo...
software
Ten Lessons Learned from Building (real-life impactful) Machine Learning Systems
(This is a blogpost version of a talk I gave at MLConf SF 11/14/2014. See below for original video and slides)
Social Networks
On Trust Networks and Gamification. Or How Quora can overcome its Hype and embrace long-term Success
5 minute read
If you are reading this blog I am pretty sure that you know quite a lot about Quora by now. If not, you should sign on and try it a bit before you continue r...