On Wed, Feb 28, 2018 at 3:17 AM, Georg Sorst <g.so...@findologic.com> wrote:

> Hi list,
> as part of a lecture on Information Retrieval I am giving we work a lot
> with Simple Wikipedia articles. It's a great data set because it's
> comprehensive and not domain specific so when building search on top of it
> humans can easily judge result quality, and it's still small enough to be
> handled by a regular computer.
> This year I want to cover the topic of Machine Learning for search. The
> idea is to look at result clicks from an internal search search engine,
> feed that into the Machine Learning and adjust search accordingly so that
> the top-clicked results actually rank best. We will be using Solr LTR for
> this purpose.
I forgot to mention, Solr is great but you might want to consider
elasticsearch as well. We release weekly dumps of the production search
indices in elasticsearch bulk imput format (json document per line) at
https://dumps.wikimedia.org/other/cirrussearch/ and have co-developed an
LTR plugin for elasticsearch that is fairly similar to the Solr one at
http://elasticsearch-learning-to-rank.readthedocs.io/en/latest/. Your task
might be easier since this is already put together, but if you are familiar
with solr it probably wouldn't be too hard to convert the elasticsearch
format into solr batch format

> I would love to base this on Simple Wikipedia data since it would fit well
> into the rest of the lecture. Unfortunately, I could not find that data.
> The closest I came is https://meta.wikimedia.org/wiki/Research:Wikipedia_
> clickstream but this covers neither Simple Wikipedia nor does it specify
> internal search queries.
> Did I miss something? Is this data available somewhere? Can I produce it
> myself from raw data? Ideally I would need (query-document) pairs with the
> number of occurrences.
> Thank you!
> Georg
> --
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