The Recommender System at Amazon.com
Get
instant personalized recommendations based on your prior purchases the moment
you log on. ~Amazon.com
Project Description AND Objectives:
The idea that recommender systems may give relevant and
useful information about what is being recommended, what is similar and
potentially useful, was the basis for our first assignment. We looked at how Amazon.com recommended
books purchased by those who had bought a particular book. However, Amazon.com seemed to have two
methods of recommending books: one for
fiction and one for non-fiction.
Fiction books recommended other fiction books by the same author, and
eventually recommended similar subjects written by authors of similar
background and of the same literary genre, typically books I already was aware
of because of the book jacket.
Non-fiction books recommended other non-fiction books of similar
subject, with a wide variety of authors.
I edlook at how this system quantifies books without
qualifying them, except in the review process, which doesn’t seem that
helpful. I say this because my own
experience with the recommender system, where I purchased four books
recommended because others bought them as they had also purchased the book I
originally purchased, was disappointing.
For example, I purchased Plein
Air Painters of the North, a non-fiction book with some excellent art
history essays and a lot of color plates of Northern Californian artist’s
works, where the subject matter was typically landscape and painted during the
period from 1885 to 1940. Amazon
recommended California
Impressionism. The reviews were
all very positive. However, the essays
in the recommended book, California
Impressionism, were not as academically focused, nor as historically
relevant, and the works tended more towards flowery still-life or portraiture
and overall, while it is a handsome book, I found it half as informative and
helpful in the research I was doing.
Amazon.com’s reviewers, anyone who wishes to publish a
review on the site, tend often to be written by those who either loved the
book, or hated it, but often the people who seem willing to go to the trouble
of reviewing are the ones who are enthusiastic about a work. Not being professional reviewers or writers
usually, they do not always articulate very well why the book is good or not
good, but rather they often pass on adulatory comments.
I compared the “recommendation” systems in real space at
book stores, to Amazon.com’s system, as well as look at a couple of other
recommended systems, to see if there might be a way to improve the
recommendation system at Amazon.com.
Areas looked at include how employees in real space stores recommend
works, what other methods of recommending stores use, and how useful those
recommendations are. Then, comparing
those real space recommendations, to online recommendations, I looked at where
the differences are or patterns of differences that might help to see where
this system is not as relevant as real space.
I also will use my personal experiences with Amazon.com
since I go to the site five or six times a week, and purchase about twice a
week. I’ve been purchasing there for
four years.
Assignment one
shows the results of the investigation into Amazon’s recommender system, where
books recommended because of an interest in Jose Saramago’s Blindness
are listed.
WHY RECOMMENDERS?
“ONLINE READERS ARE IN NEED OF TOOLS TO HELP
THEM COPE with the mass of content
available on the World-Wide Web. In traditional media, readers are provided assistance
in making selections. This includes both implicit assistance in the form of
editorial oversight and explicit assistance in the form of recommendation
services such as movie reviews and restaurant guides.” -
Marko Balabanovi´c and Yoav Shoham, “Content Based
Collaborative Recommendation”, Communications
of the ACM, Volume 40 , No. 3 (Mar. 1997)
FINAL PROJECT
REALIZATION:
Data Collection from
Real Space:
I went to Cody’s Books, and
talked with two employees. I asked each
one to recommend some fiction, based upon the fact that I had recently read Blindness,
by Jose Saramago. Neither had read this
book, but they had read others by Saramago, and were familiar with his style,
the fact that he is Portuguese, and his style of magical realism.
The first employee
recommended the following books, in no particular order (comments are the
employees):
Paul
Bowles, The Stories of
Paul Bowles
Jorge
Luis Borges, Labyrinths;
Selected Stories and Other Writings
Really Recommended.
Jorge
Luis Borges : Ficciones/Jorge Luis Borges : Stories
Jose
Luis Borges: Personal Anthology
Jorge
Luis Borges, Collected Fictions
In particular, he said that he liked “In Praise of Darkness” and “The
Book of Sand”, which are volumes of poetry.
Umberto
Eco, Foucault's Pendulum
Umberto
Eco, The Name of the Rose
VS
Naipaul, Way in the World : A Novel
Thomas
Pynchon, The Crying of Lot 49
Ralph
Elliosn, The Invisible Man Really
recommended. Said it starts wide, and
is open, and then as the subject closes and goes back in time, the style
matches this by becoming more traditional.
It also follows the path of slowly becoming more and more aware of the
blackness of the main character.
Haruki
Murakami, Norwegian Wood
The second employee
recommended the following, in no particular order (comments are the employees):
Jose
Saramago, The Year of
the Death of Ricardo Reis
She said that she had not
read Blindness, but this book was watery, it was always raining, and it was a
dreamscape. And she really recommended
it.
Salmon
Rushdie, Midnight’s Children
Distinctly American.
Manil
Suri, The Death of
Vishnu: A Novel
James
Agee, A Death in the Family
John
Banville, Mefisto : A Novel
Jorge
Luis Borges, Collected Fictions
Marthe
Bibesco, The Green Parrot : Princess Marthe Bibesco
Paul
Bowles, The Sheltering Sky
Auto-Da-Fe
-- by Elias Canetti
She said this was dense, dark, about
a man who can only see his landlady as a big blue skirt, he’s a misogynist and
hates everyone, hard to read but funny and great.
The
Archivist : A Novel by Martha Cooley
Memoirs
of Hadrian -- by Marguerite Yourcenar
It’s a first novel.
A
Bend in the River by V. S. Naipaul
The
Moviegoer -- by Walker Percy
All time classic.
A
Whole New Life -- by Reynolds Price
Hot Teen Sex.
Clockers
by Richard Price (Author)
Much better than the movie, maybe a
forgotten book as it’s very dated 80’s stuff, but really good. Harsh.
Kingdom
of Shadows -- by Alan Furst
Customers tell her they
stay up until 5am reading this book.
Bruno Schultz was a writer, who
while in a concentration camp, got into the good graces of a guard, and another
prisoner was envious and shot him. His
work was published after the war. Two
novels only. But really good.
Other Recommender
Systems:
I also looked at the
Reel.com system, just to compare and get ideas about how other systems might
work. Reel.com works much the way
Amazon.com does. It recommends movies by
the director or with actors from the movie currently being looked at, or very
similar genres. For example, looking at
The
African Queen, recommended “close movie matches” included An Affair to
Remember, Casablanca,
The Rainmaker, Summertime, and
To
Have and Have Not. All
but one have Katherine Hepburn or Humphry Bogart. But Reel.com also has “creative movie matches”, and these
included Romancing the
Stone, Rooster
Cogburn, Six Days,
Seven Nights
and Two
Mules for Sister Sara.
While these creative matches might not be the greatest choices, for one
reason or another, they are matches.
For example, Romancing the Stone is about two people who fall in love
while going through the jungle to find Kathleen Turner’s sister and to get a
large jewel. But it’s set in 1984
verses The African Queen, of 1951, which is about two people who go through the
jungle because there is no other way out, and fall in love. But they are different in sensibility in a
lot of ways, although I’m sure there are plenty of people who like both
movies. However, according to Efron and
Geisler in Is it all About Connections? Factors Affecting the Performance
of a Link Based Recommender System , Reel.com uses both a weighted system of descriptions manually placed
by Reel.com reviewers, as well as director, actor and genre information to
recommend movies. However, Efron and
Geisler have devised a new recommender system, RecEx, to try to solve this
problem, and conclude that the recommendation idea still needs evaluation.
OBSERVATIONS:
I noticed that the books
that were recommended by employees, when looked up on Amazon.com, all listed
other books by the same author under the recommended section. This confirmed my assignment results, and
those discussed in class, where the conclusion was the Amazon.com will, for a
fiction book, first recommend books by the same author, and then list books
that are by people of similar background and writing genre. The owner of a work of fiction can typically
look at the book jacket to find other books by the same author. And I was aware of the other Amazon.com
recommended books by the same author, and so the recommendation system results
just become something to move past to get to something else more useful.
The Cody’s employee
recommendations were subjective, but they made the dynamic leap that a reader
of a serious work of fiction, who might like some magical realism but also
might like some other premises as well, towards fiction, or even poetry or
short stories, that was more interesting and desirable than the Amazon.com
recommendations. It was interesting to
note that several of their recommendations overlapped either by book, or by
author, and yet, they also recommended works by women, people of other races
and distinctly different cultural experiences, and very different subject
matters. And yet, as far as the books I
was familiar with, these multidimensional recommendations made sense even
though they could not be related back (other than one time) to Saramago,
Portuguese authors, or even magical realism in exactly the same vein as Blindness.
In fact, every time I go to a bookstore in Santa Cruz on the main
street, I walk in, and ask an employee to recommend a couple of books. I’ve never been disappointed; this is how I
came upon Jose Saramago’s Blindness.
It may be that the presence of a person, emphatically communicating
their enjoyment of a book, or why they liked it or the fact that it’s a first
novel, makes more of an impression than reading an enthusiastic review
online. However, these same details are
available on Amazon.com and I believe can be exploited to make the recommender
system richer.
The human recommenders
seemed to have more insight into a broader array of multidimensional
literature, and what readers might like based on a particular choice. I found myself getting really interested in
the choices they were recommending that I was not familiar with. Could the Amazon.com system reflect a more
sophisticated recommendation system beyond just the similarity model? It might be too expensive for Amazon.com to
pay professional reviewers, or even to pay for reviews published elsewhere,
other than the editorial reviews they put above the Reviews section. However, it seems as though Amazon.com might
dramatically improve their recommender system by using a filtering system to
blend recommendations for similar books “others have purchased who purchased
this book” with a number of other pieces of information, to achieve broader and
hopefully more qualitative recommendations.
However, Amazon.com would
have to go beyond their seeming current practice of using classification of
author and book, and matching like books, to something more sophisticated and
useful. This may involve asking
returning users questions about past purchases, or blending different kinds of
information with the current classification data.
The crucial thing missing from the traditional
geographies is the failure to appreciate how environments are conceived by people as opposed to simply perceived by people.
~Munro, Hook & Benyon
Ideas
for making the AMAZON.com system better:
-
Allow returning users
to recommend books recently purchased, without necessarily reviewing them, with
a one click system. Then blend these
rankings with purchase recommendations.
This would hopefully achieve a greater sampling than the reviewers
section, which seems to attract readers of a work who really liked it, giving
it a high number of stars. The idea
would be to achieve a larger and more reliable pool of purchasing recommenders
verses the smaller pool of reviewers.
-
Allow users to chose
words and or categories to describe the work, to help Amazon.com see how users
see the works they have purchased. This
could be done after a purchase when a returning user logs on weeks later. These words and categories could be matched
to works under review by others and details like, “first novel”, “magical realism”, “historical” or “dreamscape”
could be weighted and blended with recommendations.
-
User profiles could be
blended with those lists of similar profiles for books purchased by others, to
gain a more refined recommendation.
-
After a recommended
book listing, a rating average (a la Sfgate.com’s list
of movies with critics’ ratings from 1 to 10), could be listed, averaging
Amazon reviews and critical reviews. The New York
Times Bestseller List might be used, although it has in the past been a
reflection of Barnes & Noble sales.
Maybe a book critics rating could be started in order to average
numerical critical reviews.
-
Top Amazon.com
reviewers with high helpfulness ratings and similar tastes could have their
other reviewed books blended into recommendations, and their purchases weighted
more heavily.
People are active participants in reshaping the
space. There is a dynamic relationship
between people, their activities in space, and the space itself. All three are subject to change. ~Munro, Hook & Benyon
Instead of this for
Blindness:
Customers who bought this book also bought:
We might get these
recommendations:
Customers who bought this book also bought:
Fab:
content-based, collaborative recommendation by Marko Balabanović,
Yoav Shoham, Pages: 66 – 72 Communications of the ACM Volume 40, No 3 (March
1997).
CACM special section on Recommender Systems edited by Paul
Resnick and Hal Varian, Communications of the ACM, Volume 40 , No. 3 (Mar. 1997).
Is it all About Connections? Factors Affecting the Performance
of a Link Based Recommender System by Miles Efron and Gary Geisler, August 23,
2001. Presented at the ACM SIGIR 2001
conference.
“Footprints in the Snow” by Alan
Munro, Kristin Hook and David Benyon, from Social Navigation of Information Space,
p. 1-15.
