Hi /u/AdelleChattre, just to shed some light on the TRE (Tribe Recommendation Engine), I would like to link to a screenshot I took when running your "Texas Governor Deploys State Guard to Stave Off Obama Takeover" snap through our TRE testing script which allows us to look into issues with the tribe recommendation engine. Please note that the snap in question is ran through the testing sequence as it appears now (live), so IF anything changed from the time you did not see the /t/texas tribe as a recommendation it would be hard to go back and check using older data.

As you can see from the screenshot, the tribe /t/texas is in fact the nr. 1 result, along with some other related & semi related tribes. The TKS ("tribe key score" value) is a collection of a number of factors related to the content within a snap. Details like snap title, description, meta information and keywords are all taken into account and given a scaling importance value ranging from 1 (little importance) to 10 (very important). This importance value then determines how each keyword generated using this information will be used when generating the snap keyword cloud that we use to cross-check against tribe keyword clouds which are constantly updated as more content is added to each tribe (content from snaps, text posts and or comments plays a role in building the keyword cloud). When a keyword match is made, both values are then added together and multiplied by 3 other modifiers before we generate the final TKS score.

If you're wondering why we are being especially broad with the explanation of how the tribe recommendation engine works it's for good reason, we don't want our system to be gamed. There are a number factors that play a role in what tribes are recommended, most of the time if a tribe is young and with little content the recommendation engine will not pick that tribe due to it not being as trusted as we feel it should be before it is recommended (people can and will make spam tr...

Hi /u/AdelleChattre, just to shed some light on the TRE (Tribe Recommendation Engine), I would like to link to a screenshot I took when running your "Texas Governor Deploys State Guard to Stave Off Obama Takeover" snap through our TRE testing script which allows us to look into issues with the tribe recommendation engine. Please note that the snap in question is ran through the testing sequence as it appears now (live), so IF anything changed from the time you did not see the /t/texas tribe as a recommendation it would be hard to go back and check using older data.

As you can see from the screenshot, the tribe /t/texas is in fact the nr. 1 result, along with some other related & semi related tribes. The TKS ("tribe key score" value) is a collection of a number of factors related to the content within a snap. Details like snap title, description, meta information and keywords are all taken into account and given a scaling importance value ranging from 1 (little importance) to 10 (very important). This importance value then determines how each keyword generated using this information will be used when generating the snap keyword cloud that we use to cross-check against tribe keyword clouds which are constantly updated as more content is added to each tribe (content from snaps, text posts and or comments plays a role in building the keyword cloud). When a keyword match is made, both values are then added together and multiplied by 3 other modifiers before we generate the final TKS score.

If you're wondering why we are being especially broad with the explanation of how the tribe recommendation engine works it's for good reason, we don't want our system to be gamed. There are a number factors that play a role in what tribes are recommended, most of the time if a tribe is young and with little content the recommendation engine will not pick that tribe due to it not being as trusted as we feel it should be before it is recommended (people can and will make spam tribes at one point if not already).

I hope this sheds some light on how our TRE system works, and if the results aren't as good as they should be we apologize. The TRE algorithm is a work in progress and we are constantly making tweaks to improve it.

Thanks for reading, we hope this helps.