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From Lance Norskog <goks...@gmail.com>
Subject Re: [mahout-user] Recommendation engine to support a LAMP application
Date Tue, 07 Dec 2010 23:54:02 GMT
Look at the taste-web project in Mahout. It is a recommender servlet.
https://cwiki.apache.org/MAHOUT/recommender-documentation.html ,
search for 'Standalone'.

 This is the Servlet code itself:
http://people.apache.org/~isabel/mahout_site/mahout-taste-webapp/apidocs/org/apache/mahout/cf/taste/web/RecommenderServlet.html

http://www.lucidimagination.com/search/?q=apache+mahout+recommenderservlet

2010/12/7 José Moreira <zemanel@zemanel.eu>:
> Hi,
>
> although the projects are small, for example the current only has only for
> now mostly product and category tables, the solution is intended to be
> re-used across several other existing projects and possibly some more in the
> future, that may or may not exist in the same server.
>
> Given this, my opinion is that an better solution than hacking complex
> queries and algorithms to emulate item/user based recommendations could be
> running Mahout as a dedicated service, as a custom solution may never yield
> the same result quality but it's ultimately up to the customer (and his
> budget).
>
> I'm currently a bootstrapping unemployed developer (from Portugal) and
> although i could use the revenue, i'm ultimately interested in providing an
> efficient solution and make the customer happy and so i'm open to include
> professional support as i currently don't have experience with Mahout. I was
> told that possibly Sean Owen was providing that kind of support, how can i
> reach him? And/or is there anyone else providing paid support? If so i can
> be contacted directly by this e-mail.
>
> Meanwhile i'll keep drilling the documentation.
>
> 2010/12/7 Sebastian Schelter <ssc@apache.org>
>
>> Hello Jose,
>>
>> I think Mahout has everything you need. All you have to do is to record
>> user-item interactions after which you can use one of our many recommender
>> implementations. If you wanna do recommendations based on similar user
>> tastes you can use a userbased recommender, if you'd like to look at
>> item-similarities you can use an itembased recommender. It's very likely
>> that you need to play around a little with both to see which one fits your
>> needs best.
>>
>> You can also implement a custom similarity measure that takes content
>> similarities like matching descriptions into account and rescores the
>> computed similarities based on that. Last but not least connecting the
>> taste
>> webservice to PHP shouldn't be a problem either.
>>
>> I suggest you get a copy of "Mahout in Action" from
>> http://manning.com/owen/and read the recommender chapter to get a more
>> detailed impression of what
>> Mahout currently offers and then talk with your customer to decide whether
>> it fits your needs.
>>
>> --sebastian
>>
>> PS: If you decide to use Mahout, don't forget to put your application on
>> the
>> Powered By page! :)
>>
>> 2010/12/7 José Moreira <zemanel@zemanel.eu>
>>
>> > Hello,
>> >
>> > i was asked by a friendly company to improve their current product
>> > recommendation system which is currently a couple of complex SQL queries
>> > using fulltext search that match similar products based on title,
>> > description and tags (a single comma separated field) and i don't have
>> > experience with recommendation engines.
>> >
>> >
>> > The new system must also take into account the click rate of a product
>> > (in regard of similar ones) when it's being recommended, meaning that
>> > when products B and C are presented as similar products of A and the
>> > user (a web visitor, the application has no user accounts) clicks on B,
>> > it's click rate in regard of A is increased and may influence the next
>> > recommendation of similar products of A which orders the result by
>> > similarity and click rate in relation to the displayed product, which i
>> > think is called item-to-item recommendation.
>> >
>> > Eventually, similar personal "tastes" will also need to be implemented.
>> >
>> > As a note, my customer is also interested in obtaining for example 8
>> > products from this set plus 2 more with a low click rate to give them a
>> > chance of obtaining a higher click rate.
>> >
>> > It's also required to be able to configure the weights of the
>> > "variables", for example, 0.5clickRate+0.3description and so on.
>> >
>> > Instead of starting to hack a custom solution i've been looking for
>> > recommendation engines in PHP (the database is mysql) but haven't found
>> > much.
>> >
>> > I'm also aware of Apache Mahout, i could suggest the use of Mahout or a
>> > similar java engine as a "web service" on a separate server, although i
>> > know the customer is very much prone to a "pure" PHP solution, as their
>> > applications are very basic stack-wise. The end solution will possibly
>> > be implemented on other similar projects, so perhaps the standalone
>> > server would be running multiple instances.
>> >
>> > Additionally i don't have experience with Mahout or recommendation
>> > engines in general, so i'm currently not aware how they manage the
>> > "related click rate" feature. My questions are:
>> >
>> > Any suggestions on implementing these features as a stand-alone server
>> > or otherwise? I apologize if i haven't described the situation using
>> > more direct technical terms.
>> >
>> > Thank you
>> >
>> > --
>> > http://zemanel.eu
>> > http://github.com/zemanel
>> > http://pt.linkedin.com/in/josemoreira
>> > http://djangopeople.net/josemoreira
>> > irc://zemanel@irc.freenode.net
>> >
>> >
>>
>
>
>
> --
> http://zemanel.eu
> http://github.com/zemanel
> http://pt.linkedin.com/in/josemoreira
> http://djangopeople.net/josemoreira
> irc://zemanel@irc.freenode.net
>



-- 
Lance Norskog
goksron@gmail.com

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