Recommendations and rating systems

This is something that came out of my IIMB class this morning. We were discussing building recommendation systems, using the whisky database (check related blog posts here and here). One of the techniques of recommendation we were discussing was the “market basket analysis“, where you recommend products to people based on combinations of products that other people have been buying.

This is when one of the students popped up with the observation that market basket analysis done without “ratings” can be self-fulfilling! It was an extremely profound observation, so I made a mental note to blog about this. And I’ve told you earlier that this IIMB class that I’m teaching is good!

So the concept is that if a lot of people have been buying A and B together, then you start recommending B to buyers of A. Let us say that there are a number of people who are buying A and C, but not B, but based on our analysis that people buy A and B together, we recommend B to them. Let’s assume that they’ve taken our recommendation and bought B, which means that these people are now seen to have bought both B and C together.

Now, in case we don’t collect their feedback on B, we have no clue that they didn’t like B (let’s assume that for whatever reason buyers of C don’t like B), but in the next iteration, we see that buyers of C have been buying B, and so we start recommending B to other C buyers. And so a bad idea (recommending B to buyers of C, thanks to A) can spiral and put the confidence of our recommendation system in tatters.

Hence, it is useful to collect feedback (in the form of ratings) to items that we recommend to customers, so that these “recommended purchases” don’t end up distorting our larger data set!

Of course what I’m saying here is not definitive, and needs more work, but it is an interesting idea nevertheless and worth being communicated. There can be some easy workarounds – like not taking into account recommended products while doing the market basket analysis, or trying to find negative lists and so on.

Nevertheless, I thought this is an interesting concept and hence worth sharing.

2 thoughts on “Recommendations and rating systems”

  1. You need not always collect the ratings explicitly. Let’s say a Netflix-like site recommends a movie. Even if the person watching does not rate that movie, you can derive a substitute for ratings based on a) did he abandon the movie stream before completion b) if so, at what stage did he abandon etc.

    I wonder what such implicit ratings would be for drinking whiskies though.

    1. Someone like Netflix or Pocket or Flipboard can do this easily! so you don’t need explicit ratings. Even Amazon for Kindle books.

      but what about Amazon for paper books? Or something else (like whisky) where you can’t implicitly measure satisfaction?

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