Hi all, reviving this thread.
For those of you who use an external file for your suggestions, how do you
decide from your query logs what suggestions to include? Just starting out with
some exploratory analysis of clicks, dwell times, etc., and would love to hear
from the community any advise.
David,
True! But we are hoping that these are purely seen as suggestions and that
people, if they know exactly what they are wanting to type/looking for, will
simply ignore the dropdown options.
On 1/24/20, 10:03 AM, "David Hastings" wrote:
This is a really cool idea! My only concern
Hi Audrey,
As suggested by Erik, you can index the data into a seperate collection and
You can instead of adding weights inthe document you can also use
LTR(Learning to Rank) with in Solr to rerank on the documents.
And also to increase more relevance with in the Autosuggestion and making
This is a really cool idea! My only concern is that the edge case
searches, where a user knows exactly what they want to find, would be
autocomplete into something that happens to be more "successful" rather
than what they were looking for. for example, i want to know the legal
implications of
Hi Audrey,
As suggested by Erik, you can index the data into a seperate collection and
You can instead of adding weights inthe document you can also use LTR with
in Solr to rerank on the features.
Regards,
Lucky Sharma
On Fri, 24 Jan, 2020, 8:01 pm Audrey Lorberfeld - audrey.lorberf...@ibm.com,
Hi Alessandro,
I'm so happy there is someone who's done extensive work with QAC here!
Right now, we measure nDCG via a Dynamic Bayesian Network. To break it down,
we:
- use a DBN model to generate a "score" for each query_url pair.
- We then plug that score into a mathematical formula we
Erik,
Thank you! Yes, that's exactly how we were thinking of architecting it. And our
ML engineer suggested something else for the suggestion weights, actually -- to
build a model that would programmatically update the weights based on those
suggestions' live clicks @ position k, etc. Pretty
