Because there's a 101 plus 1, 102.
And we're going to get, 3.02.
Which, really no different, to one decimal place the same as three, so.
That person who came and entered a five star rating
didn't really have much of an effect at all, alright.
So, that's why we say it's less susceptible to harsh and lenient reviews.
And some people, the difference between, you
know, spam review or harsh lenient review.
Harsh reviewers would be people who tend to
enter really low ratings for a lot of products.
So, if you took, if you looked at them, and you looked at all the
products they ever rated, it would tend to be lower than a lot of other people.
Maybe they're more critical.
Lenient reviewers, on the other hand, would tend to enter, very high reviews.
So they'd, you know, they'd enter all five for everything.
This is the greatest product ever, you know?
This doesn't really give you much information
if they always enter the same review.
And, as we said also, spam reviewers would be, for instance if it's your product
and you wanted to make it seem like it was much better, so more people bought it.
Or, if you were a competitor to a product, you would maybe want to
give a lot of really low star ratings just to try to downgrade your competition.
So now, if we look back again in our example up here, we see that the,
the Toshiba was, did have a lower average
rating but again it was based on 95 review.
So instead of just so quickly choosing the Panasonic
we may very well now choose the Toshiba instead.
Because for something like a TV we may
want more than eight people to have rated it.
And 4 and 4.5 aren't that far apart in reason.
Continuing on a point of harsh and lenient reviews.
Note that we can't just throw out all five
star ratings, or throw out all one star ratings.
Because some of them are going to be very accurate.
For instance, Amazon has this thing called most helpful reviews.
The most helpful favorable, and the most helpful critical review.
And, you can see that a lot of people find each one helpful.
So clearly, some people are, are gathering
some valuable information from each of those reviews.
In this case right here, we have one that's a four star review.
104 of 106 found it helpful.
And in this case, it's a one star review.
So it's a very bipolar reaction to the product.
but, a lot of people are finding each one helpful.
So we, we can't just throw those out, we have to still factor them in.
So, all these beg the question of can
how we enhance the reliability of average reviews?
And in general, we can identify two approaches to this.
The first one is to enforce quality.
Right.
So, to do that we want to screen out all of these quote, unquote bad ratings.
we, if we, if we see a spam rating we, we don't want to include it.
If we see something that we know is harsh, or harsher than normal,
maybe we want to take it out, but we can't always do that per say.
So it's a little ambiguous as to how exactly to do that.
But one way, for instance, is to require people to actually submit,
their name or to only be able to submit a review once, right?
So, not be able to rate anonymously, right?
So, if you're, if you're entering some spam review or if
you're trying to sabotage a product, you wouldn't be able to
do that as easily if you can't if you have to
enter your name and you have to indicate who you are.
And more generally, we have to check the
mechanism that's used to enter the reviews, right?
So.
Who's able to review?
Do you have to buy the product first, before you can review?
How many times can you review?
You know, all these questions need to be answered in order to enforce quality.
Another one, too, is what is the scale of the review.
And an interesting point of this.
Studies have been done which have shown that depending upon the, the scale whether
it be one to three one to five star, one to ten or so forth.
They illicit different psychological responses in the users.
So, they may actually come up with
averages relative to the maximum that are different.
And to, to kind of give insight in to why that is, right, if, if it's, if
the scale, which is one, two or three, right, if you're trying to do a mapping
from this scale to, one through five, for instance, you may say okay, well one is
the lowest therefore that's going to map down to
this one here which will also be the lowest.
And three is the highest, which is going to
map to five here which is the highest.
Then that means that two could either be a two, three, or a four on this scale.
And it's, you know, it's not clear, so.
It's going to you know, you could have
different results depending upon what the scale is.
The second one is to consider the review population size.
Which is going to be something that we're
going to consider quantitatively throughout this lecture.
As we saw before, the question is, how large is large enough?
So, how large does the population size have to be?
Does it have to be 50 people, 100 people, 1,000 people
in order for us to be able to trust that average rating?
