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Many sites, such as youtube and other video sites and online stores have a 'recommended for you' section.

Is there any data available on how often these are used?

On youtube I always click the 'uploads only' checkbox because I'm usually not interested in those (although there can be good things in there), but if few people use them they just clutter up the page.

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    Are you thinking of doing this in a current project?
    – Erics
    Commented Dec 8, 2011 at 2:37
  • Personally, I do that for Amazon.com and Goodreads but I'll typically skip that for all other sites.
    – Alan Ho
    Commented Dec 8, 2011 at 5:04
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    It depends on the algorithm. For Amazon, I ignore the recommendations because it shows things too similar to what I just bought. If I already bought headphones X, why would I need another headphones Y? On the other hand, Youtube's algorithm thrives on similarity to your previous likes or favorites. It has always done a good job of learning my preferences. I am often recommended bodybuilding videos because the software has learned that I often watch those videos and like them.
    – JoJo
    Commented Dec 8, 2011 at 8:22
  • @JoJo: good points. That could have been an answer rather than a comment, I feel.
    – Kris
    Commented Dec 8, 2011 at 12:08

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Like any feature, the usage is almost entirely dependent upon the quality of the implementation. It is very difficult to create a good recommendation engine; if you manage to do so, and repeat visitors find it useful, they will use it.

Anecdotally, I have used these features in Amazon and NewEgg. I have found little value in YouTube recommendations, because my noise ratio is high (I look at lots of videos that were linked from sites like Reddit, but few on my own). But I do look at the feature, because it is prominently placed despite the low quality of the results.

I would strongly suggest creating your site so that a recommendation is entirely optional to the experience. When you have less data to work with, the recommendations will be of low quality. Once you have a breadth of data to mine on your users, you should (note that the 'should' involves a whole lot of 'it's hard, but doable') be able to create a feature like this useful to your visitors.

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The effectiveness of a 'recommendation' section will vary massively depending on the implementation. But that doesn't mean there aren't certain things that can help. Good ideas include:

  • making the value of the content visible immediately. Rather than an expandable 'stuff you might like' section, actually show some of the content and make it apparent how good the material is
  • a sensible position in the workflow. This will vary a lot with your site and its content, but typically speaking, it's good practice to put recommendations towards the end of content, when users have already evaluated that your material is relevant to them. Youtube handles this by putting recommendations in the video space once the movie has ended.
  • a gaming-resistant algorithm. If users see your recommendations channel covered in spam, or content that merely looks like spam, they will quickly learn to distrust that content channel.
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