arekp

Did you contact them before smearing them in public?

I submitted all other links before to HN (5000 best movies, books, websites, etc.)

I had a small feature - importing ratings from Netflix in my (now abandoned) application "Movie Galaxy" http://5000best.com/movie-galaxy/

It lists only websites in English, and the ranking is US-centric.

It's interesting to compare the findings with website popularity: http://5000best.com/websites/

Yea, that Excel-table-like design probably breaks all design rules. It surprises me most when I occasionally see design professionals complementing it. I am rather a backend programmer and I have no illusions.

But even the Hacker News audience does not have enough geekiness to understand my previous attempt at a movie recommender system: http://5000best.com/movie-galaxy/

The website should withstand several times larger traffic than the HN frontpage effect. I am not sure about the reddit effect.

I do not know when I will have time to experiment more with the website, but volunteers are always welcome.

I have to tune that one. Violence and blood alone should not indicate a horror.

Thank you. I was in top 50 in the Netflix Prize and wrote a book about recommender systems (http://arek-paterek.com/book/) if someone is interested how the bread is made.

Score is different in each of the 55 rankings. I do not know if this is clear in the interface, but "Law" is one of the rankings, not a filter.

Yes, the interface became a little complicated. The previous version was more accessible for a casual user, for example the following article about the website was received pretty enthusiastically (retweeted about 1000 times) http://gigazine.net/news/20121012-5000-best-movies/

I guess the right way to fix it is to hide some features and add a tutorial.

Great feedback. I see I should do it this way.

Thanks. We'll see. Each feature costs developer's time.

Edit: the stack is G-WAN and everything is written in plain ANSI C. I wrote my own HTTP servers in C/C++ for my previous websites, but this time I tried G-WAN. I think it saved me one week of work or two.

The website is running on the cheapest virtual dedicated server from 1&1 with 25% of one processor time. The total cost is less than $10 monthly.

The Netflix Prize winners had a few RBM models that used the dates.

Regarding the DBM - I also tried to use more than one layer, and without success. I tried out 3-layer and 4-layer autoencoders (can be called 1.5-layer and 2-layer DBM), with initialization by stacked RBMs or without it. It did not work well probably because: a) the model was inaccurate, and b) the learning method proposed for DBM was not completely correct. Intuitively, the right DBM-like model with the right learning method should have a chance to improve something on the Netflix task.

I found some improvement though (rather learning time than accuracy) in the standard RBMs. Instead of using CD, I split the weights into two sets, creating a directed RBM version. The "up" weights from the visible nodes to hidden are learned with CD with T=1. The "down" weights are learned to best fit the visible nodes, using the hidden nodes as predictors. The hidden nodes generated by CD T=1 are good enough, and we do not need additional iterations with increased T.

Extending my comment, I am almost sure that passing a small negative argument (which is converted to a large positive number) causes malloc to return NULL on Linux.

False. malloc() fails with a negative argument.

This makes me think. In 2012 people still think that genetic programming, a numerical optimization technique (and not the most efficient one), magically solves random machine learning problems.

It is there, but undocumented and the syntax may change in the future. http://5000best.com/movies?g2=on&y0=1990&y1=2005&...

I had to choose one name per movie. If a movie is based on a novel, I believe the author of the novel should be credited in the first place. Usually the script degrades the quality of the novel.

It is doable, but because these companies compete with each other, would they all be happy seeing their streaming links next to each other? They can cut off access to their APIs at any time.

Thanks for the suggestions and for the bug report.

The clustering was based on the regularized SVD / matrix factorization results. Details in my e-book.

So which taste the best? I started in the Netflix Prize, and for me a movie taste is a point in a 32-dimensional space or 64-dimensional space. Which point is better than the others?