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This
holiday season, when we Google for the most trending gifts, compare different
items on Amazon or take a break to watch a holiday movie on Netflix, we are
making use of what might be called 'the three R's' of the Internet Age: rating,
ranking and recommending
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This holiday season, when we Google for the most
trending gifts, compare different items on Amazon or take a break to watch a
holiday movie on Netflix, we are making use of what might be called “the three
R’s” of the Internet Age: rating, ranking and recommending.
Much like the traditional “three R’s” of
education – “reading,
’riting and ’rithmetic” – no
modern education is complete without
understanding how websites’ algorithms combine,
process and synthesize
information before presenting it to us.
As we explore in our new book, “The Power of Networks: Six Principles
that Connect Our Lives,” the three tasks of rating, ranking and
recommending are interdependent, though it may not be initially obvious. Before
we can rank a set of items, we need some measure by which they can be ordered.
This is really a rating of each item’s quality according to some criterion.
With ranked lists in hand, we may turn around and
make recommendations about specific items to people who may be interested in
purchasing them. This interrelationship highlights the importance of how the
quality and attractiveness of an item is quantified into a rating in the first
place.
Ranking
What consumers and internet users often call
“rating,” tech companies may call “scoring.” This is key to, for example, how
Google’s search engine returns high-quality links at the top of its search
results, with the most relevant information usually contained in the first page
of responses. When a person enters a search query, Google assigns two main
scores to each page in its database of
trillions, and uses these to generate the order for its results.
The first of these scores is a “relevance score,”
a combination of dozens
of factors that measure how closely related the page and its content
are to the query. For example, it takes into account how prominently placed
search keywords are on the result page. The second is an “importance score,”
which captures the way the network of webpages are connected to one another via
hyperlinks to quantify
how important each page is.
The combination of these two scores, along with
other information, gives a rating for each page, quantifying how useful it
might be to the end user. Higher ratings will be placed toward the top of the
search results. These are the pages Google is implicitly recommending that the
user visit.
Rating
The three Rs also pervade online retail. Amazon
and other e-commerce sites allow
customers to enter reviews for products they have purchased. The star
ratings contained in these reviews are usually aggregated into a single number
representing customers’ overall opinion. The principle behind this is called “the
wisdom of crowds,” the assumption that combining many independent opinions
will be more reflective of reality than any single individual’s evaluation.
Key to the wisdom of crowds is that the reviews
accurately reflect customers’ experiences, and are not biased or influenced by,
say, the manufacturer adding a series of positive assessments to its own items.
Amazon has mechanisms in place to screen out these sorts of reviews – for
example, by requiring a purchase to have been made from a given account before
it can submit
a review. Amazon then averages the star ratings for the reviews that
remain.
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Websites'
algorithms use the 'new 3 Rs' to combine, process and synthesize information
before presenting it to us. Researches from Princeton University explains these
points are ranking, rating and recommendation
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Averaging ratings is fairly straightforward. But
it’s more complicated to figure out how to effectively rank products based on
those ratings. For example, is an item that has 4.0 stars based on 200 reviews
better than one that has 4.5 stars but only from 20 reviews? Both the average
rating and sample size need to be accounted for in the ranking score.
There are even more factors that may be taken
into consideration, such as reviewer reputation (ratings based on reviewers
with higher reputations may be trusted more) and rating disparity (products
with widely varying ratings may be demoted in the ordering). Amazon may also
present products to different users in varying
orders based on their browsing history and records of previous
purchases on the site.
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Before
items can be ranked, they need a measure by which they can be ordered This is
really a rating of each item's quality according to some criterion With ranked
lists in hand, websites may turn around and make recommendations
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Recommending
The prime example of recommendation systems is Netflix’s
method for determining which movies a user will enjoy. Algorithms predict
how each specific user would rate different movies she has not yet seen by
looking at the past history of her own ratings and comparing them with those of
similar users. The movies with the highest predictions are those that will then
make the final cut for a particular user.
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| Amazon has mechanisms in place to screen out these sorts of reviews – for example, by requiring a purchase to have been made from a given account before it can submit a review. They also make recommendations based on products you have purchased in the past |
The quality of these recommendations depends
heavily on the algorithm’s accuracy and its use of machine learning, data
mining and the data itself. The more ratings we start with for each user and
each movie, the better we can expect the predictions to be.
A simple rating predictor might assign one
parameter to each user that captures how lenient or harsh a critic she tends to
be. Another parameter might be assigned to each movie, capturing how
well-received the movie is relative to others. More sophisticated models will identify
similarities among users and movies – so if people who like the kinds
of movies you like have given a high rating to a movie you haven’t seen, the
system might suggest you’ll like it too.
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The
prime example of recommendation systems is Netflix's method. Algorithms predict
how each specific user would rate different movies she has not yet seen by
looking at the past history of her own ratings and comparing them with those of
similar users
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This can involve hidden dimensions that underlie
user preferences and movie characteristics. It can also involve measuring how
the ratings for any given movie have changed over time. If a previously unknown
film becomes a cult classic, it might start appearing more in people’s
recommendation lists. A key aspect of dealing with several models is combining
and tuning them effectively: The algorithm that won the Netflix Prize competition of
predicting movie ratings in 2009, for example, was a blend of hundreds of
individual algorithms.
This combination of rating, ranking and
recommendation algorithms has transformed our daily online activities, far
beyond shopping, searching and entertainment. Their interconnection brings us
clearer – and sometimes unexpected – insights into what we want and how we get
it.
Originally published in The Conversation and then Daily Mail, UK
Originally published in The Conversation and then Daily Mail, UK
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