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I am trying to determine the apt algorithm for a ranking problem that I am working on. I have social media metrics - engagement, sentiment, audience size etc for several brands and am looking for a ranking / classification algorithm to rank them.

I am not sure if I have a dependent variable or label class for classical classification algorithms.

The data is aggregated by brands and algorithms needs to rank the brands based on the metrics.

Any ideas would be much appreciated.

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  • $\begingroup$ Who is going to be presented and evaluate the ranked list, and what is the feedback going to look like? You have described auxiliary signals but not the main one that is going to drive the ranking function, as I understand it. $\endgroup$
    – Emre
    Commented Sep 13, 2017 at 20:41
  • $\begingroup$ Brands. It's is essentially a rank/score of brands' relative performance of social media activity. @Emre $\endgroup$
    – kms
    Commented Sep 13, 2017 at 20:45
  • $\begingroup$ The ranking function is driven by the metrics mentioned in the question - engagement, sentiment, audience size etc. The score/rank needs to be an indication of the relative performance of the brands. Hope this helps clarify. $\endgroup$
    – kms
    Commented Sep 13, 2017 at 20:50
  • $\begingroup$ I don't see room for machine learning without some feedback on the list, so could you give us an example? Do you want to develop some sort of "Klout score"? $\endgroup$
    – Emre
    Commented Sep 13, 2017 at 20:53
  • $\begingroup$ Alexa ranking algorithm is a good example. alexa.com/siteinfo. Their algorithm ranks domain based on a few site metrics. $\endgroup$
    – kms
    Commented Sep 13, 2017 at 20:57

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That problem is can be framed as learning to rank. In order to use learning to rank algorithms, each data example needs to have a label which is the relative ordering of results.

If you do not have labels, you will have to use unsupervised learning. For example, clustering similar activity together.

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