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Closed 10 years ago.
I'm looking for a solution for collaborative filtering in rails or even possible examples. So far I have only found acts_as_recommendable which looks useful but I noticed it hasn't had any updates in the last 2 years.
Does anyone know of any other solutions and/or examples?
Have you evaluated Apache Mahout? It is a Java based solution, with HTTP access to recommendation engine.
Reference:
Introducing Mahout
This pertains to the examples part of your question, as both the libraries mentioned below are in Java.
The article referenced in above answer, written by Apache Mahout-Taste library author, has neat examples, source code of the examples( using 2.5GB wikipedia data) , and an excellent packaging to run and see those examples in action, in a few minutes.
Apache Mahout-Taste
The specific section is Building a recommendation engine
Here is another open source recommendation engine.
easyrec
In 2013, there's the ActiveRecord Reputation System gem by Twitter. There's also a free RailsCast on the topic.
here is a 50 line recommendation system in Ruby: http://www.igvita.com/2007/01/15/svd-recommendation-system-in-ruby/
there is one link there which doesn't work (when he says "mathies click here" it points to Using linear algebra for intelligent information retrieval by Berry et al.: http://www2.denizyuret.com/ref/berry/berry95using.pdf )
Related
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Closed 9 years ago.
I want to use Neo4j for my web applications. I am using Node.js for the server side. As I understand, I will have to learn Cypher to query the database.
On the website I could find only a Reference guide. I'm looking for some complete tutorial, at least for the introductory part, regarding the integration of this into the website development process. I don't know Java and I don't want to learn it at the moment.
Thanks for sharing!
Having some SQL knowledge, this webinar will take you step by step from 0 to beginner: http://watch.neo4j.org/video/60292144
After this, you will have to refer to your own server-side language to complete the integration, but that should be the most effortless part.
Here are some useful beginner knowledge sources:
Watch the webinars about Cypher on:
http://watch.neo4j.org/
Play around in http://console.neo4j.org
Check out the various node.js drivers that support Cypher--I believe the Thingdom one is the most popular:
https://github.com/thingdom/node-neo4j
Check out examples that use Cypher on Max's blog:
http://maxdemarzi.com/category/cypher/
Luanne posted an excellent guide to query optimization (which may be a bit advanced for newbies, but you should still read it), as well as other Cypher-related posts on the same blog:
http://thought-bytes.blogspot.com/2013/01/optimizing-neo4j-cypher-queries.html
Also, I've posted two introductory blog posts about Cypher just recently, and plan to post more:
http://wes.skeweredrook.com/cypher/
The Graph Databases book:
http://graphdatabases.com/
Feel free to post your newbie Cypher questions here--I'll set this to a community wiki in case anyone has any more resources to add.
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Closed 10 years ago.
Hi although this is not technical question, it is important to know where can someone find best from the experts.I am new in JQuery, I am using http://jquery.com/ for learning purposes. But it would be appreciable if I could have some better reference learning site for JQuery where videos and demos are available
So presuming you're somewhat familiar with jQuery, I would advise starting with JavaScript.
Code Academy run through the whole thing and you will understand JavaScript
jQuery's official site has a long, comprehensive list of tutorials. Pick the ones you think are down your alley and run with it.
After that, there are many, many good sites dedicated to jQuery.
Some that you might like are:
http://www.learningjquery.com/
http://jqueryfordesigners.com/
http://visualjquery.com/1.1.2.html (doesn't teach, just think of it as visual docs for the framework)
The New Boston jQuery Video Tutorials
This is a very good site.
I have gone through many tutorials and they are extremely helpful
Just try few videos first and see how much you get from it.
http://listic.ru/jQuery_Cookbook.pdf
found this quite organised and useful.
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Closed 10 years ago.
This question is too general for answers directly in stackoverflow (see this closed question) however I think the community would beneift from having starting points for high quality external articles which can dive into the topic in more detail.
I'd like to see the most informative article on Grails 'Best Practices' based on the following criteria:
how to split tasks between controllers/views/services/domain objects
testing
security
when to use plugins
database design considerations
topics specific to Groovy or Grails that would not be part of general best practice guides
Please answer with a link to an external article adding just a short summary. Please do NOT comment too much on whether you think individual ideas/patterns are good otherwise this question will be closed for 'soliciting debate and discussions'
This InfoQ article on Grails best practices has an excellent list of how to split logic between controllers/views/services/domain, testing, config and other Grails specific considerations.
It's a short article and I'd recommend it to any Grails developer
The GrailsPodcast website just had a good 'best practice' session: http://www.grailspodcast.com/blog/id/249 (currently offline)
There is a presentation by grails persistence expert Burt Beckwith explaining potential performance issues with out-of-the-box usage of hasMany/belongsTo mappings in GORM:
http://burtbeckwith.com/blog/files/169/gorm%20grails%20meetup%20presentation.pdf
Here's an example of how to apply one of the cases that Burt exposes:
https://mrpaulwoods.wordpress.com/2011/02/07/implementing-burt-beckwiths-gorm-performance-no-collections/
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Closed 10 years ago.
I am looking for a book where some monocular/visual SLAM is described and implemented.
Can you list and recommend some?
I'd like to use OpenCV but that is not a requirement.
I don't know of a book with a description of such an algorithm, but there's a complete open source implementation (in C++) of a vslam system available as part of the Robot Operating System. It uses SURF descriptors and vocabulary trees for place recognition, and bundle adjustment for SLAM. It does use OpenCV heavily as it's made by the same people. See the website here. I can't say for sure as they don't mention and I haven't looked in great detail, but their implementation seems to be based on, or at least is similar to, this paper.
Edit: The paper linked above was actually written by the people who implemented the vslam system given above, it appears. So it is definitely a good resource for understanding it.
I don't know about a book, but maybe PTAM can be useful. The ISMAR 2007 paper by Klein has a description about the system and the source code is available so you can check the details.
Of course, PTAM is just a (good) method in the SLAM field.
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Closed 11 years ago.
I need a good stemming algorithm for a project I'm working on. It was suggested that I look at the Porter Stemmer. When I checked out the page on the Porter stemmer I found that it is deprecated now in favor of the "Snowball" stemmer.
I need a good stemmer, but I can't really spend significant time implementing (or optimizing) my own. What is the best "off the shelf", freely available stemmer? Are there any non-free stemmers available for a reasonable price? Or, is the Snowball stemmer my best bet?
The Porter2 stemmer is the one I've decided to go with. It seemed the porter stemmer was the standard, but when I found the page by the author he recommended the "Snowball (Porter2)" stemmer. There is a C port link on this page.
It really depends on how you're planning to apply it. The Natural Language Toolkit (http://nltk.sourceforge.net) has a number of stemmers implemented in it that should be able to handle most applications. I prefer the Morphy stemmer.
Of course, it's available in Python, so if you're working with another language, you can always look through the code to glean the algorithm and transfer it to your language of choice. Python is highly readable.