Selection bias and recommendation systems

Yesterday I was watching a video on youtube, and at the end of it it recommended another (the “top recommendation” at that point in time). This video floored me – it was a superb rendition of Endaro Mahaanubhaavulu by Mandolin U Shrinivas. Listen and enjoy as you read the rest of the post.

I was immediately bowled over by youtube’s recommendation system. I had searched for both Shrinivas and Endaro … in the not-so-distant past so Youtube had put two and two together and served me up an awesome rendition! I was so happy that I went to town twitter about it.

It was then that I realised that this was the firs time ever that I had noticed the top recommendation of Youtube. In other words, every time I use youtube, it recommends a video to me, but I seldom notice it. And I seldom notice it for a reason – they’re usually irrelevant and crap. The one time I like the video it throws up, though, I feel really happy and go gaga over the algorithm!

In other words, there’s a bias which I don’t know what its exactly called – the bias that when event happens in a certain direction, you tend to notice it and give credit where you think it’s due. And when it doesn’t happen that way, you simply ignore it!

In terms of larger implications, this is similar to how legends such as “lucky shirts” are born. When something spectacular happens, you notice everything that is associated with that spectacular event and give credit where you think it’s due (lucky shirt, lucky pen, etc.). But when things don’t go your way you think it’s despite the lucky shirt, not because the shirt has become unlucky.

It’s the same thing with belief in “god”. When you pray and something good happens to you after that, you believe that your prayers have been answered. However, when you pray and something good doesn’t happen, you ignore the fact that you prayed.

Coming back to recommendation systems such as Youtube’s, the problem is that it is impossible for a recommendation system to get recommendations right all the time. There will be times when you get it wrong. In fact, going by my personal experience with Youtube, Amazon, etc. most of the time you will get your recommendation wrong.

The key to building a recommendation system, thus, is to build it such that you maximise the chances of getting it right. Going one step further I can say that you should maximise the chances of getting it spectacularly right, in which case the customer will notice and give you credit for understanding her. Getting it “partly right” most of the time is not enough to catch the customer’s attention.

Putting marketing jargon on it, what you should focus on is delighting the customer some of the time rather than keeping her merely happy most of the time!