Dealpluscoupon.com have many famous Featured Stores, including Amazon.com

1. Information and information last time
systems • we considered some general features
of what makes Google successful
Module 2 – • and you were asked to consider
successful information systems
Ian Ruthven
office LT12.16
email: ir@cis.strath.ac.uk
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some of your reasons some of your reasons
• compared to physical shops • good user interface
– can send products anywhere in world – searching, browsing and suggestion facilities
– site features such as reviews – neutral colours
• compared to other e-commerce sites • bad user interface
– localised shopping (.co.uk, .com, etc)
• saves costs on shipping, shop in different languages
– cluttered, confusing, lots of scrolling
• but consistent layout across different parts of site and • reviews on products
different domains
– breadth of database and connection to other sellers • advertising vs. no advertising
• good: can find things Amazon does not stock directly, used – can gain revenue (c.f. Amazon mastercard)
items are cheaper – some groups argued lack of advertising increases trust
• bad: maybe confusing for people (think buying Amazon, but
buying from someone else) – confusion over how much advertising Amazon does
– ease of use (one-click shopping for example) • reliability (or at least perceived reliability)
– security (also ability to track purchases makes site feel • personalisation
more secure)
– wish lists, recommendations, personalised stores, etc
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Amazon competitors
• www.bookshop.co.uk (Internet
• Amazon’s tasks Bookshop – pre-Amazon, now WHSmith
– user task: to allow you to buy things owned)
– developers’ task: to sell you things • 3 ways to find
• but lots of sites do this, so why is books
Amazon successful? • search
• start with user tasks • browse
• offers
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2. browse Waterstones (same thing)
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others Amazon (mostly same thing)
• most e-commerce systems offer the same features
• search
– good for directed finding
– bad for general finding (what is there, what might I like)
• browse (categories)
– good for general finding
– ok for directed finding
• but can be bad (slow) for directed finding
– bad for large data sets (too many categories - where is
something stored, is there something new…)
• special offers
– small number of specially displayed items
• good for new items (informs user)
• good for popular items (saves time)
• bad for almost everything else
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several things make Amazon
also
successful
• v. large database • some fun things
– including out of print and used books
• not restricted catalogue – like different representations of books
– not just buying from Amazon but a network of booksellers – concordance (100 most common words)
• with reputations like e-bay
• c.f. Google one answer better than none especially for repeat – which book?
business • again among another away behind ben black boat cam
– not just books e captain come cried day dead doctor door down end
enough even eyes face far first flint found get go goo
• one stop shop – web supermarket d got great hand hawkins head heard himself hispanio
• useful features la house island jim john know last lay left little livese
y long look looked man may men might moment mr
– reviews by readers, readability scores, sample pages must nothing now old once own place put right round
– thinking about what information a consumer might want rum saw say sea see seemed seen ship should side s
• and what might be more difficult about buying things without ilver sir soon squire still sure take tell thing think ho
seeing them ugh thought three till time told took treasure two upo
• nice features n voice water went word
– filter searches by price, genre
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3. others more importantly
• statistically improbably phrases • also good support
– north inlet, apple barrel, seafaring man, – can return things just because you
seafaring men changed your mind
– wish lists for nice friends to buy you
• capitalized phrases (CAPs): presents
Ben Gunn, Long John, Captain Smollett, Black Dog, Admiral
Benbow, John Silver, Jim Hawkins, Skeleton Island, Cap'n • but more importantly
Smollett, Billy Bones, Captain Flint, Israel Hands, George – recommendations
Merry, Cap'n Silver, Doctor Livesey, Tom Morgan, Cape of
the Woods, Job Anderson, Tom Redruth, Darby M'Graw, • uses idea of collaborative filtering
Davy Jones, Execution Dock, Narrative Continued – based on similarity of people’s taste
• all can be used as search keys • “If Crawford likes Scooby Doo, I will like
Scooby Doo”
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Collaborative filtering
Collaborative filtering
(two levels)
• collaborative filtering based on user
profiles tracking
• what books/cds/dvds/etc people have bought
• what web pages they have visited
• what food they buy (supermarkets)
• aim: to recommend items to people
who share similar tastes
• also called recommender systems
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collaborative filtering ratings
• can also include ratings
– explicit vs implicit
1 2 3 4 5
2 12 8 9 7 1 2 3 4 5
1 3 5 4 6 2 12 8 9 7
• has similar tastes to
• has v. different tastes
to 1 3 5 4 6
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4. collaborative filtering two problems
• suggests things you did not even know • size of data
you wanted…. – based on most similar person
• need large group
– and therefore you buy more things • need relatively large number of ratings/purchases
(system goal) • ‘cold-start’ problem
– and buy them from Amazon – new items to database
• particularly useful for complex objects • cannot recommend without previous use/purchase
• cannot purchase without recommendation
– how to describe what kind of dvds you like – new people
or what type of music?
– usually need some hybrid system
• easy to recognise harder to search for • querying/browsing for new items, recommendation for
others
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Amazon Amazon
• Amazon, like Google, is trying to capture a task
market • some you did not mention
– Amazon = buy things – no physical stock control issues
– Google = search for things
• tries to make shopping
– no costs on physical buildings (in
– easy expensive shopping areas)
• one-click, delivery to many addresses, gift-wrapping etc
– lower costs on staff
– successful
• wide database, recommendation • it’s not a real shop!
– happen more often
• recommendation, wish-lists – competition is not just online
– lightweight
• fun things to keep you interested and interacting with the site
– so therefore you buy more things!!!!
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next time next time
• you have a choice between two new- • you have a choice between two new-
ish but popular sites…. ish but popular sites….
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