Awesome, I think I could learn a lot from you.
Do you have a decent amount of user data? Sounds like you have a ton.
I noticed that information retrieval problems fall into a sort-of layered
pyramid. At the topmopst point is someone like Google where the sheer
amount of high quality user
Sorry, as I was saying, the machine learning approach, is NOT limited to
having lots of user action data. In fact having little or no user action
data is commonly referred to as the cold start problem in recommender
systems. In which case, it is useful to exploit content based similarities
as well
I totally agree that it depends at the task at hand and the amount/quality
of the data that you can get hold of.
The problem of relevancy in traditional document/semantic information
retrieval (IR) task is such a hard thing because there is little or no
source of truth you could use as training
BTW, as i mentioned, the machine learning
On Monday, May 4, 2015, J. Delgado joaquin.delg...@gmail.com wrote:
I totally agree that it depends at the task at hand and the amount/quality
of the data that you can get hold of.
The problem of relevancy in traditional document/semantic information
Hi Doug and Joaquin,
This is a really interesting discussion. Joaquin, I'm looking forward to
taking your code for a test drive. Thank you for making it publicly
available.
Doug, I'm interested in your pyramid observation. I work with academic
search which has some of the problems unique
Doug,
Thanks for your insights. We actually started with trying to build off of
features and boosting weights combined with built-in relevance scoring
http://www.elastic.co/guide/en/elasticsearch/guide/current/scoring-theory.html.
We also played around with replacing and/or combining the default
Hi Joaquin
Very neat, thanks for sharing,
Viewing search relevance as something akin to a classification problem is
actually a driving narrative in Taming Search http://manning.com/turnbull.
We generalize the relevance problem as one of measuring the similarity
between features of content
Here is a presentation on the topic:
http://www.slideshare.net/joaquindelgado1/where-search-meets-machine-learning04252015final
Search can be viewed as a combination of a) A problem of constraint
satisfaction, which is the process of finding a solution to a set of
constraints (query) that impose
