Article on A/B testing

Hello folks!

Just previous week I came across an interesting article from Netflix about A/B testing the article. It drew my attention because I have been also thinking recently about how to shorten the time/users needed for A/B testing. I encourage all of you to read it.

One thought I immediately got (and posted there) is that as you go though the courses and conferences you hear and read that the impression of the whole list matters. So in this approach what would be the limit to which the two tested algorithm outputs can differ? If there are too different would the interleaved list as a whole seemed to be a "random" draw from the items to the end user?

I don't have answers, just the feeling that if you test too different outputs then it might seem too "random" to the user. Anybody has thoughts? I am curious to hear your opinions.

Books on recommender systems

Hello fellow enthusiasts,

I just came across a quora post about what books are out there on recommender systems. It seems we are gettier luckier and the amount of literature is nicely increasing. The answers are from known names who, for example, present at RecSys so I recommend looking into the books they suggest.

https://www.quora.com/Do-you-know-a-great-book-about-building-recommendation-systems

Stuff from the internet on evaluating RS

Hello recommender engine enthusiasts,

Just completely accidentally came across this great answer from Xavier on quora on the topic of evaluating the recommender systems and the importance of A/B testing. Here is the link https://www.quora.com/How-do-you-measure-and-evaluate-the-quality-of-recommendation-engines

Inside his response he mentions other very interesting articles on his paper and on talks of other people, recommend reading it.

YouTube recommender system article

Hello All,
have been doing some browsing around the Internet and found this interesting article on the YouTube recommender system. It is not one of the super newest but interesting so posting it in case someone finds it useful.

Link