Not sure if this would classify as a comment or an answer, but it's useful information nonethelss:
So in reading this question I HAVE to point this out - ever heard of the paper?:
Arvind Narayanan and Vitaly Shmatikov. "Robust De-anonymization of Large Datasets (How to Break Anonymity of the Netﬂix Prize Dataset)".
The University of Texas at Austin February 5, 2008.
Full text is at:
It's quite a famous paper and was even on the news when it got published.
Here's the abstract:
We present a new class of statistical de-anonymization attacks against
high-dimensional micro-data, such as individual preferences,
recommendations, transaction records and so on. Our techniques are
robust to perturbation in the data and tolerate some mistakes in the
adversary’s background knowledge. We apply our de-anonymization
methodology to the Netﬂix Prize dataset, which contains anonymous
movie ratings of 500,000 subscribers of Netﬂix, the world’s largest
online movie rental service. We demonstrate that an adversary who
knows only a little bit about an individual subscriber can easily
identify this subscriber’s record in the dataset. Using the >>>Internet
Movie Database<<< as the source of background knowledge, we successfully
identiﬁed the Netﬂix records of known users, uncovering their apparent
political preferences and other potentially sensitive information.
I looked at their citations for clues but they only thing they cite verbatim is:
IMDb. The Internet Movie Database. http://www.imdb.com/, 2007.
This is also quite a while ago too. However going through the full text of that article, you may be able to glean some clues as to how they got their data and replicate those - so this could potentially help you.