Thanks for your reply.

I have a "like" system (you can only like, not dislike) as well but I also 
want to track views as another measure of popularity. It doesn't have to be 
super exact but would be great if it filtered out bots of course.

I just thought this was a quite common issue and wanted to know how other 
people solves it.

The features I would like is:
1. Filter bots.
2. Count unique requests based on session hash, ip address or similar.

Perhaps I need to build something custom to get this.

Linus



Den söndagen den 18:e augusti 2013 kl. 02:07:07 UTC+2 skrev Phil:
>
>
> On Saturday, August 17, 2013 2:04:51 PM UTC-7, Linus Pettersson wrote:
>>
>> Hi
>>
>> Often when I build sites I need some way to track visits to specific 
>> pages. For instance I need to see what Artists are the most popular so I 
>> can let my users sort based on popularity.
>> I have used the gem Impressionist before but recently I ran into major 
>> performance issues (see 
>> https://github.com/charlotte-ruby/impressionist/issues/94).
>>
>> I do use Google Analytics and _could_ pull that data somehow I guess, but 
>> isn't that data pretty "slow" (I want the views to update at least in a few 
>> minutes)?
>>
>> So, what do you use to track views in your apps?
>>
>
>
> FIrst I would say you need to crack the nut of what "popular" means. 
>  Simple URL hits may not be your best metric for that.  It could mean how 
> long a user dwells on a page, or how many scroll all the way to the bottom, 
> who's logged in vs. who's a casual users or an indexing bot, etc.
>
> Assuming "artists" is a table, you could add a simple counter column 
> (maybe with a related "counter_reset_at" column with a timestamp) and 
> increment it when the appropriate criteria is met.
>
> When I first made a popular T-shirt site (I'll refrain from saying the 
> name here) we used to have a 'Like' feature similar to Facebook.  We found 
> users would systematically set up new accounts and like themselves and 
> dislike everybody else to try to falsify their ratings.  We came up with 
> some simple ways to weed out the bad data.
>
> Hope this helps.
>
>
> Phil
>

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