Psychology of Preference & Rating -- Interview with Martijn Willemsen

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From the course by University of Minnesota

Introduction to Recommender Systems: Non-Personalized and Content-Based

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University of Minnesota

Introduction to Recommender Systems: Non-Personalized and Content-Based

379 ratings

Course 1 of 5 in the Specialization Recommender Systems

This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations. After completing this course, you will be able to compute a variety of recommendations from datasets using basic spreadsheet tools, and if you complete the honors track you will also have programmed these recommendations using the open source LensKit recommender toolkit. In addition to detailed lectures and interactive exercises, this course features interviews with several leaders in research and practice on advanced topics and current directions in recommender systems.

From the lesson

Course Wrap-up

We close this course with a set of mathematical notation that will be helpful as we move forward into a wider range of recommender systems (in later courses in this specialization).

Unified Mathematical Model13:10

Psychology of Preference & Rating -- Interview with Martijn Willemsen31:43

Meet the Instructors

Joseph A Konstan
Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
Michael D. Ekstrand
Assistant Professor
Dept. of Computer Science, Boise State University

Welcome back, in this video we have a guest with us Martijn Willemsen

from the Eindhoven University of Technology.

Martijn studies the ways that judgement and

decision making aspects of psychology interact with recommender systems.

And he'll be talking with us about how the psychology of preference relates to

the ratings that we collect from our users.

Martijn welcome to the class.

>> Hi there.

>> So, first question.

MovieLens asks me what I think of the movie The Iron Giant and

I tell it four and a half stars.

What just happened?

>> That's a good question, I think.

So, you gave a rating, but what does it mean to provide a rating?

I don't know actually.

So what does the scale entail?

So you gave a rating on a five star scale.

And from a psychological perspective I think rating is

just a judgement of some property.

And I think in this case the rating is all about the quality of the movie.

Or how much you like it.

Now, what is interesting about the MovieLens system,

it uses these five-star scales that we now see a lot on the internet.

And the MovieLens system actually is a little bit better because it also allows

for half stars, so it's actually a ten-point scale.

It goes from 0.5 stars to 5 stars.

I've been wondering how it gets to these stars scales.

Because you nowadays see them all over the place but I actually don't know.

I have looked a little bit at this but I don't know where they come from.

It's like in psychology we've been using scales for many years.

We call them Likert scales and quite often they're also five point scales.

And we use them for to ask people about their attitudes and stuff like that.

But, why we use stars, I don't know.

I think there's some marketing behind this.

Because what is nice about this star scale is that,

even one star doesn't really look bad.

It's, it's at least a star.

Okay, but that's about the stars itself.

We're talking here about a rating, a judgment in the psychological terms.

If I look at the rating scale from a psychological perspective,

I would say it's a unipolar scale.

And looking at the stars,

it sort of suggests that it's just about the amount of goodness.

So one star is a little bit good, five stars is really good.

But that also depends on the labeling, I think, they use.

So I actually checked MovieLens,

there have this new MovieLens version that is out now for two years or so.

And I couldn't find any indication what the stars mean.

Of course we all understand that five stars is better than one star.

But then going back to the old MovieLens, because I remembered it had a scale,

I checked that out.

And there it says that one star is awful,

three stars says it's okay, and five stars says must see.

That's a very interesting labeling, I think.

But it at least suggests that there's more to this than just being

a unipolar scale that runs from a little bit good to very good.

Because actually the one star,

it says awful which is almost bad I would say, it's a very negative phrase.

And that is also interesting that if you compare these

five star scales with other scales we often also use bipolar scales.

For example if I ask you a survey and ask you to respond to a statement

to which you can agree on we typically use bipolar scales.

We say I disagree, or I agree, and I could be somewhere in the middle, neutral.

Not sure whether we have this four or five star scale for the MovieLens.

Now, I've been talking quite a lot now already about the scales and the stars and

so, it will really depends on how you interpret the stars.

But I think what is more important is to think about what it means,

what it means to give such a rating.

The most important aspect, I think,

with these type of scales is the absolute nature of this judgement.

So you ask me for a quality judgement or a liking judgement,

but you do that in an absolute sense.

And what do I mean by absolute is that there is no comparison,

there is no reference point.

You just ask me how good this is but you don't give me any

reference to other movies or whatever.

And that's also, I think, why ratings are a bit funny.

Because it's actually quite a hard task to respond to this question,

to give a rating on a scale.

If you look at people that build recommender systems,

they sort of assume that if I ask you a rating that reflects your, sort of,

stable preference for something, your stable liking.

But what we often find in psychology, but also, by the way,

when we evaluate recommender systems and look at what sort of ratings people give,

is that these numbers are actually quite unstable.

And that sometimes I might give Iron Giant 4.5 stars, and

then another moment I might give it a 4 star, or

5.0 star.

So is this really a stable expression?

Is it something that basically you get out of your head so

you have these ratings stored?

And for every movie that you have seen there is a little slot in

your memory that also stores your preference, your rating for that movie.

I don't think that's the case.

And we have had some psychology, some discussions in psychology about it.

So Baruch Fischhoff already long ago,

makes the distinction between what he calls basic values and articulated values.

And the idea is that actually for a lot of assessments that we make,

we actually articulate how we like it at the moment we are asked about it.

We might have some basic value, some basic idea of how we like particular things or

what we think about particular things.

But most of our evaluations or expressions

are actually only generated on the spot.

So to give you an example I think,

I might have some basic values for particular food items.

So I might actually like hamburgers and I might actually always like them.

So I have a strong basic value for hamburger.

But whether I will like a particular Chinese dish

might depend on many other factors.

And I might like Chinese in particular but at a particular moment,

actually might like it not that much.

I'm not sure this is the best example [LAUGH] actually but

maybe you should go back to the movie domain.

I think the question is, are there movies

that we really value in a very stable way?

Actually, I don't know The Iron Giant that well.

I guess you like it because you gave it a 4.5 star.

>> [LAUGH] >> But

is this something that would be a basic value to you or

would it be something that might differ on the context?

>> It'd probably differ a little bit on the context,

especially are we wanting to watch something with lots of explosions tonight,

then it's probably not very suitable.

But most of the time, yeah, let's watch the Iron Giant, it's suitable for

a wide variety of situations certainly.

>> Yeah so, I think in terms, if you talk about evaluation,

like this, we have to take into account that this rating

therefore will not be always the same.

And that it will very much depend on the particular associations that come to

mind when you think about this particular object.

I think that is what is really important.

So for this assessment, you have to rely on what comes to mind,

what you're memory brings you when I asked you that question.

How would you rate this?

And of course,

the sort of associations that come about wil differ from moment to moment.

And therefore, cause some deviations in your ratings from one time to another one.

And that might differ in the movies.

So I think movies that you really liked, you will always like.

But there might be movies in between that you will like when you sit down with your

friends having beers and a good night out.

Or when you would sit on the couch with your girlfriend,

going to watch a romantic comedy that you would ever watch with your friends.

And that with your wife it might be a five star and

with your friends it might be three star.

Of course this makes sense but

that means that the ratings we give to a system might not be that stable.

And the question is, does it mean that our preference is not stable?

I don't know, I think that part of that,

there are some components of our preferences are sufficiently stable.

But it's actually always two aspects.

We have some basic likings and

dislikings and some aspects that come about depending

on the context that are contingent as we say in psychology on the situation.

Now, if you look at the literature in recommender systems,

there has been some discussion about this, because, that as we notice that,

that if you ask people to re-rate particular item.

That you often get different numbers.

And I know, that a lot of our colleagues actually call this rating noise.

And that they are looking for ways to reduce the noise or

estimate how much inherent noise there is in rating systems so we can basically know

how accurate our predictions can be in the end.

But I wouldn't call it noise.

I think because we're pretty sure where these deviations

might be coming from although we might not actually know the mechanism at this time.

But it's I think worthwhile to investigate because if we know

where the deviations come from we might actually adapt to it and

adjust our system such that they take this into account.

I think it's important to realize that this one number that a user gives you for

an item, is there is much more behind it than this one number.

It's a proxy for their liking but, keep in mind it's not more than that.

>> Yeah, and it's, you talked about quality and liking and

those dimensions are not necessarily aligned.

Like when I think about the way I use the ratings scale,

I personally usually rate mostly for quality.

But that means that a lot of my five star movies are movies that are really good and

I'm glad that I watched them, like the Pianist or Schindler's List.

Or other things that if I just want to have fun on a Friday night,

you're a lot better off recommending a four star movie like The Avengers.

>> Yeah, sure. [LAUGH]

>> So the fun and quality are kind of

collapsed into this one judgement that I'm being asked to make,

and then we're recommending based on.

>> You have to take into account that there is maybe

a too simple model behind these ratings.

But, anyway.

>> You've talked about the various things that cause ratings to

deviate from one time to another and be,

at least our observations of these judgments being unstable and

how a lot of recommender systems research has classified this as rating noise.

But what are some of the things that make a rating more or less reliable,

and what can we do in our systems to try to get more reliable,

preference judgments from users.

>> Yeah so as I told you already that a lot of this rating behavior

depends on the sort of associations that people have when they give ratings.

I think the role of memory shoots not the, disregarded that much.

And that is quite often.

So, when I start thinking about it, is when I entered this field,

I was quite surprised at a lot of the systems that we use typically when people

start using the system they asked you a bunch of ratings.

Because the system has to learn your preferences.

You get a whole list of movies or other items depending on them.

System of course and you're just asked to rate these items.

But quite often these might be items that you have seen quite long ago.

If you take MovieLens, for example,

it might ask me to rate The Shawshank Redemption, which is a great movie,

although I can hardly pronounce its name.

And I know a lot of the MovieLens users gave it a five star and

I think I gave it a five star as well because I really like it.

But it really depends on how much I can still remember of that movie.

So we once did a study to test for this.

And what we did was very simple.

We basically took a set of like 150 movies

that we knew were aired on the Dutch Public Television

in the months prior to our experiment.

So we know that some people would have seen these movies quite recently.

But most of these movies if they aired on public television they're not very young.

They're typically are like five years old or four years old.

So a lot of people would also have seen this movies when they came out and if I

ask them now to rate them, they would have to rely on their memory from the past.

So that's the experiment that we did.

We gave them this list of movies, we asked them to rate 15 or

20 of these movies that they had seen before.

And we also asked them, and

that's different from the standard recommender systems,

we also asked them to indicate how long was it ago that you saw this movie.

So a simple scale, I think we had six or seven categories, like one week,

one month ago, three months, six months, a year, two years, longer ago.

Something like that.

Now first,

although a lot of these movies had been on television the month before the study.

Most people actually reported that they had seen these movies quite long ago,

like three or four years or so.

But fortunately there were some people that had seen it in the last six months,

or last three months.

So we looked at their ratings and what was interesting is that the same movie

might actually get a four or five star, if people had seen it recently.

Whereas they would only get a three star or

sometimes even a two star when they had seen them like three or four years ago.

So looking at the distributions of the ratings of these movies,

all the movies that the people had indicated had seen recently,

we had many four- or five-star ratings.

Whereas all the movies that people said were three years or

longer ago had mostly three-star ratings, and some even two and one.

So looking at the rating distributions, it almost looked like

the ratings were regressing towards the average, the three star or so.

And this is a problem I think for those systems.

Because if you ask me to rate a bunch of movies when I start using your system.

You probably will give me a lot of these old movies.

And then I will probably give a lot of three and

four stars, one of them five stars.

And you might actually, your system might actually be recommending me movies.

You're actually trying to predict how much I would like this movie in

three years from now.

Not how much I will like it tonight.

[LAUGH] Or something like that.

I'm not really sure what will happen but

the predictions might not be as good as you want them to be.

What is happening here, I'm not really sure and

this is still something I will actually like to investigate.

But it's quite hard to do that, because you, basically how can you?

You would need a study where you can actually know pretty sure that there

are some movies that some people have seen recently and some are not.

But whatever, I think what is happening here is that in general,

when you have recently seen a movie, you have a vivid memory of that movie.

And to be able to give a good rating,

you have to come up with some reasons for that rating.

And if you recently saw it, your vivid memory will give you all sorts of

good reasons why it might be a good movie.

There might be great special effects.

There might be this famous actress.

All that stuff is gone after three years.

You probably might remember the lead actor and

you might perhaps know the director of that movie.

You might vaguely remember that it was quite funny but you won't

remember the exact jokes that people made that really made you laugh a lot.

So therefore, your rating might actually go more towards the middle of the scale,

that would be my guess.

So it's clear that I think the reliability of these ratings might

very much depend on the reliability of your memory, that's for sure.

So I think this gives you part of the answer because you were asking me both

about the reliability of ratings but also how we might actually improve on this.

I think that this is a rhetorical question because we have a very nice paper on

this that we wrote together.

With some other people in GroupLens where we actually did a study on trying

to provide rating support for users.

And the thing we did in this paper and

I think that the thing we did in this paper, is that we tried to give

users some support while providing ratings through the system.

And the study was like this, so we had a bunch of movies that people would rate,

we would give them support doing this rating.

And then a few weeks later they came back and we asked them to rerate the movies,

I think, if I recall correctly, but you would know the details as well.

And we had two different support systems, I remember.

So one was actually based on this idea that our memory

might need some help when we do rating.

So one thing we did is to give people any relevant tags.

So, I think what we did is, the MovieLens system has this tag genome.

And we can provide tags for a movie, and I think what was really cool about this

system is we actually gave people tags that would fit with them personally.

And those tags would describe aspects of the movie.

And given my story about all the associations that come about when you

think about ratings, we hoped that this tags would actually help people to have

better associations with that movie that they had seen previously.

And therefore would be more consistent in their ratings.

It didn't help [LAUGH] That much, I remember.

What did help was the other version that we had.

So there we gave exemplars.

So basically, we had people when they hover at the scale.

So when they would hover over four stars for example, we would show them a movie

they had rated before and that would also have been rated four stars.

So we actually would give examplars across the entire rating scale.

Using previous ratings that people have given.

And again, what was really nice about how we did that in the paper is we

just didn't give any movie that this user rated four stars.

But we would try to find movies that were also similar to the movie they are rating.

So because yeah if I'm rating some romantic comedy and I've hovering

four stars and then you give me Die Hard as an example of a four-star movie.

Sure that is an exemplar for four stars, but it's not a good exemplar for

that romantic comedy.

So we actually made sure that that didn't happen.

And I remember that did work quite well.

First I think users thought this was really useful, although

they had a bit of trouble initially understanding what was happening.

And we also could show that their re-ratings were more consistent,

if I recall correctly.

So I think that was a nice example of how you can

help ratings to become more consistent over time.

>> So that ties into my next question of with that we're allowing users

to do the rating by comparing movies.

And saying do I like this movie the same as, more, less than Die Hard,

to decide where to go on the ratings scale.

>> Yeah you're right so we talked about absolute ratings but

we're actually giving, it's not just absolute any more.

We're giving some baseline or some reference point already.

Yeah that's a good point. >> So

I know there have been some research papers particularly and I think that's

been used in a few deployed systems where Rather than asking the users for

these absolute judgements of rate this apartment, four stars.

We asked them instead which apartment listing do you like better,

and we asked them series of these comparative questions.

How does the comparative question psychologically differ from the absolute

how much do you like this movie or this apartment listing, question?

>> Yeah, I think it's a completely different question actually.

And people in decision psychology have recognized this for many years already.

But let me first go back to this idea of rating.

So I've been using the phrase preference quite a lot already

as what ratings indicate.

So a lot of people in recommending systems think ratings reflect preferences.

But if you really look at the term preference

then there's actually something else.

Because preference means to prefer something and that's a relative statement.

I would say I prefer A over B.

You could perhaps say I prefer A, but

I think by nature preferences have some relativity.

And that's true for our cohortative system as well.

I think most our judgments are relative judgments, not absolute judgments.

And that's also why I needed so much time to talk about ratings and why they are so

weird.

Because I think in general, it's way easier for us to say I like this

over that, than to say I like this 4 stars, and this 4.5 stars.

And to give you a clear example of this and

a difference between the two, let's say I've been rating some movies.

And let's say for example The King's Speech, you know that movie?

It's a nice movie so I rated it four stars.

And the next time I looked at Grand Budapest Hotel.

I'm not sure how familiar that is in the US but

it was quite a famous movie two years ago in Europe.

I might also rate that fours stars so

I have two movies I give them both are given four star ratings.

Now does that mean I like them equally well?

If you ask me which

do you like better the King's Speech or the Grand Budapest Hotel I'm

pretty sure I liked The Grand Budapest Hotel much better than The King's Speech.

So even though both get four stars,

I still can indicate which of these movies I prefer.

So in a way, any relative preference might actually be more informative as well.

One reason why that is, is because if you do

a relative comparison you have more fine grade skill.

You can actually base your judgement more on smaller differences between the items.

To give you a little bit of psychology behind this, and

let's take one step away from movie recommendations,

let me tell you a little bit about a classical research on this.

Most of this research was done by Chris Shee who was a psychologist at Chicago.

I think the business school over there in Chicago.

And he in the 90s he had this nice distinction between what he calls joint

evaluation and separate evaluation and he did some very interesting experiments.

For example, he would ask people let's say you want to hire a programmer.

Let's use the context that is easy to understand for people in Computer Science.

And he has this computer programmer, he has a GPA of 2.5,

and he has written 10 programs.

What's the salary?

How much would you pay him per year for this job?

Now they ask a lot of bunch of people about another programmer.

Let's say a programmer does a GPA of 4.

And he has to make three programs, how much that are you, will you pay this guy?

I think for most people GPA is an easy measure to evaluate of

a GPA of course a 4 is much better than 3.5.

However, how much programs a particular person has written so the experience and

what that means is much more difficult to evaluate.

So what happens if you ask people separately, they'll probably pay the guy

with a higher GPA much more salary than they would give the guy with a lower GPA.

But if you put in next to each other, we have one guy with somewhat lower GPA but

he has written ten programs.

And another guy that's a higher GPA but he has only written three programs.

You probably going to pay much more money to that guy that has much more experience.

So in joint evaluation when you see the two next to each other, you some

attributes that are hard evaluate when you see them on their own now become very easy

to evaluate And you can easier see the difference and better judgment upon that.

So, I think comparing or

relative judgments are much different from absolute judgments.

It's not necessarily so

that relative judgments are always better than absolute judgment.

I think what the point, for

example, that Chris Hsee makes is that in normal life, we are more often

in separate modes in separate evaluation more than in joint evaluation modes.

So let's say you go to a shop and you see two televisions.

And in this job you see that one television

actually has a slightly better picture quality than the other one.

If you see them next to each other, it's easy to see that small difference.

But the one that has the better picture quality actually

is not a very nicely looking television.

>> But you bring it home, because you know it has the better picture quality.

But when you're at home, yeah.

You still look at a beautiful picture quality, but

you cannot see the difference anymore of it except for the other one.

But you're also looking at this ugly television that doesn't have good looks.

So I think it's important to find these relative differences in some cases,

but in other cases, they might actually not help you that much.

But i do think that when people make judgements,

it's just easier for them to do this relatively, to say i like this over that.

Then to give absolute ratings.

>> Thank you for taking the time to be with us and

I think our students are going to find this very interesting.

>> Okay, thanks a lot for having me and I hope I'll get some more insights

into the psychology behind the ratings >> And preferences.

And have fun with the course.

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