Hi,
I've been asked how to use Solr as a component in a machine learning
system, doing document comparison based upon feature vectors.
If I have two vectors, one in the index (in some form) and one in the
query (in some form), how can I do, for example, a vector multiplication
of the two vectors
I'm not sure if Solr is the right tool to do this task. You probably need a
machine learning library like Mahout or Weka.
PS: Lucene doesn't really use Cosine Similarity, it's using a practical
TF-IDF Similarity.
Nicholas Ding
On Wed, Nov 26, 2014 at 3:05 PM, Upayavira u...@odoko.co.uk wrote:
Thanks Nicholas, there is a sense in which Solr isn't the right tool.
However, we already have lots of business rules encapsulated into filter
queries, and already have content ingestion pipelines for our content in
place.
TF-IDF similarity is pluggable (even just by sorting on function
queries),
Upayavira,
on the lucene list, two tools are sometimes talked about which might be doing
some of what you are searching:
- semanticvectors (https://code.google.com/p/semanticvectors)
- word2vec https://github.com/kojisekig/word2vec-lucene/i
Maybe it helps?
I'm under the impression that you are
Hello,
Lucene rocks in calculating scalar product (a score of whatever similarity)
of sparse feature vectors. That's it. Note that 'feature' usually means a
term, and 'feature vector' is a document. Which might be opposite to your
problem definition. You can either expand the definition of your
