The Five-Point Rating House of Cards

The web is awash with 5-point rating schemes.  Netflix, Amazon, YouTube, WordPress (via PollDaddy ratings), Apple’s Ap Store, the Android Market and countless blogs use them to gauge people’s experience with various items.  It’s not hard to see why 5-point schemes are so popular;  they are really simple to implement, familiar to most people, and can be made to look all kinds of pretty using icons for stars, hearts or smileys.

Unfortunately, they often don’t gather very useful data.  And that’s a big problem for sites that intend to use ratings as the backbone of their recommendation systems.

The 5-point schemes common on the web suffer from two core problems: nonresponse and measurement bias.  First, many people choose not to rate many items, and those that do tend to have had a positive experience.  Data from YouTube supports this, as does that from Netflix.  Second, the scales are usually not labelled, meaning people answer under a wide variety of interpretations as to what each ‘point’ means.  This comment from a YouTube user suggests ambiguity in the scale can also exacerbate the nonresponse issue…

Ratings on YouTube have always been somewhat confusing for me: should I rate the content of the video or the quality? There are some wonderfully shot videos on YouTube that really don’t have any meaningful content, and there are also a lot of videos that have wonderful content but are shot very poorly. I think a dual content vs. quality rating would add too much complexity to the system, but I often don’t rate a video for that very reason.
I don’t find ratings all that helpful, probably due to the fact that there are millions of people using YouTube, each with a different opinion. It doesn’t influence whether I watch a video, but then again, I usually find videos from friends or other channels I respect.

Ratings on YouTube have always been somewhat confusing for me: should I rate the content of the video or the quality? There are some wonderfully shot videos on YouTube that really don’t have any meaningful content, and there are also a lot of videos that have wonderful content but are shot very poorly. I think a dual content vs. quality rating would add too much complexity to the system, but I often don’t rate a video for that very reason.

I don’t find ratings all that helpful, probably due to the fact that there are millions of people using YouTube, each with a different opinion. It doesn’t influence whether I watch a video, but then again, I usually find videos from friends or other channels I respect. [comment found here]

Probably the best way to get around these problems is to measure a person’s preferences indirectly by recording their behaviour: how much of the video did they watch?  did they share the content? did they look for related items?  It is fairly well established that what people say and what they do can be very different things, so users’ actions may be much more useful than their words.  Certainly, the ‘popular’ and ‘most viewed’ categories in YouTube appear to rely on behavioural metrics, so perhaps their rating metric is redundant.

However, the ‘measure behaviour’ solution is best suited to organisations that deliver interactive material consumed on-site (YouTube, StumbleUpon).   So, what can you do if you are dealing with items that aren’t consumed on-site? Collapsing the scale to “liked it”/”didn’t like it” won’t solve the core issues – if anything it will just mean you give up what little discriminative power the 5-point scale might have had.  Another suggestion is to expand the scale to 10 points. While this may increase the discriminative power of the scale and is a format people are familiar with, it won’t solve the ambiguity problem.  For that you need to construct clear labels that are likely to be interpreted in much the same way by most people.  Ideally, the scale will also relate as directly as possible to whatever it is you want to use the data for. This is much easier said than done, but here is an example that might work for a site recommending local restaurants:

0 – I will definitely not (0%) eat there again soon

1 – It is unlikely (20% chance) I will eat there again soon

2 – There is some chance (40%) I will eat there again soon

3 – There is a good chance (60%) I will eat there again soon

4 – It is quite likely (80%) I will eat there again soon

5 – I will definitely (100%) eat there again soon

This is actually a heavily butchered version of a probability-based predictive instrument called the Juster Scale.  It would have to be tested, but it at least serves to demonstrate the qualities I outlined above.  The scale could also easily be extended to more points (in fact, the Juster Scale is an 11-point scale).

Finally, there is the issue of nonresponse.  A good scale will help resolve this, but ultimately you need to follow-up users to increase rating participation.  TradeMe and TravelBug are two local examples that do this well.  You’ll never get every user giving a rating for the products they’ve tried, but at least you’ll bump the proportion up, which will provide a more solid foundation for any recommendation or imputation algorithms you want to run over the data.

So, if you are at the early stages of developing a rating function for your site, give some careful thought to how your scheme will work.  Test it out before you commit to it longer term.  Doing so will give you much better data to work with down the track.

One final point: you can probably forget all this if your core reason for implementing ratings is to generate reassuring sales cues to prospective buyers (i.e., in the same way sites put testimonials up to reassure users).  In that case, you are likely to be better off with an unlabeled 5-point scale.  As the folks at YouTube found, most of the ratings you will get with such a scale will be positive!

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