Recommender Lecture

Recommender Systems

Customization

• Customization is one of the more attractive features of electronic commerce.– Creating a different product for every user, suited to

his/her tastes.

• Once thought to be a novelty, now essential– Provides a way for online providers to compete with

brick-and-mortar competitors.– Possible to serve niche markets.

• Bezos: “If I have two million customers on the Web, then I should have two million stores on the Web”– (how dated is that? )

How can personalization help?

• Turn browsers into buyers– People may go to Amazon without a specific purchase

in mind.– Showing them something they want can spur a

purchase.

• Cross-sales– Customers who have bought a product are suggested

related products.

• Encourages Loyalty– Amazon is interested in becoming an e-commerce

portal. This means that they would like to respond to all your online purchasing needs.

Examples

• Amazon– Featured Recommendations: tailored to past

views/purchases.– People who bought this: compares customers – Alerts- sends you email when stuff you like is

on sale.– Customer reviews– ListMania

• Allows users to add their own reviews of products.• Customers can find other reviews by a given user.

Examples

• Netflix– You rate movies and others are suggested based

on these ratings.– You are compared to other users.

• Reel.com– Movie Matches – you enter a movie, and it

suggests similar movies.– Compares movies to movies.

Examples

• Citeseer– Recommends papers based on citations, similar

text, cited by.

• Launch– Lets you customize your own “radio station”.

• You get a customized mp3 stream

Types of recommendations

• Population-based– For example, the most popular news articles, or

searches, or downloads.– Useful for sites that frequently add content.– No user tracking needed.

• Netflix: Movers on the top 100– Reflects movies that have been popular overall.

Types of recommendations

• Item-to-item– Content-based– One item is recommended based on the user’s

indication that they like another item.• If you like Lord of the Rings, you’ll like Legend.

• Netflix: 1-5 star rating.– Estimates how much you’ll like a movie based

on your past ratings.

Types of Recommendations

• Challenges with item-to-item:– Getting users to tell you what they like

• Both financial and time reasons not to.

– Getting enough data to make “novel” predictions.

• What users really want are recommendations for things they’re not aware of.

Types of recommendations

• Item-to-item– Most effective when you have metadata that

lets you automatically relate items.– Genre, actors, director, etc.

• Also best when decoupled from payment– Users should have an incentive to rate items

truthfully.

Types of recommendations

• User-based– “Users who bought X like Y.”– Each user is represented by a vector indicating

his ratings for each product.– Users with a small distance between each other

are similar.– Find a similar user and recommend things they

like that you haven’t rated.

• Netflix: “Users who liked …”

Types of recommendations

• User-based– Advantages:

• Users don’t need to rate much.• No info about products needed.• Easy to implement

– Disadvantages• Pushes users “toward the middle” – products with

more ratings carry more weight.• How to deal with new products?• Many products and few users -> lots of things don’t

get recommended.

Types of Recommendations

• Manual/free-form– Users write reviews for a product, which are attached to

the product.

• Advantages:– Natural language, explanations for pros/cons, users get

to participate.

• Disadvantages:– Few ‘neutral’ recommendations, difficult to automate.

• Netflix: Member Reviews, Critic Reviews

Potential Applications

• Placing a product in space– “The product you’re looking at is like …”

• Configuring display– Choosing what to show or emphasize based on

preferences.

• Personalized discounts/coupons– Grocery stores do this.

• Clustering users– Determining the tastes of your consumers.

Details: How RS work

• Content-based (user-based) systems try to learn a model of a user’s preferences.

• This is a function that, for each user, maps an item, to an indication of how much the user likes it.– Might be yes/no or probabilistic.

How RS work

• A common model-learner is a naïve Bayes classifier.

• An item is represented as a feature vector.– Web pages: list/bag of possible words– Movies: list of possible actors, directors, etc.

• This vector is large, so common features are filtered out. (the, an, etc)

• Useful for unstructured data such as text

Naïve Bayes Classifier• Maps from an input vector to a probability of

liking.– Naïve: assumes inputs are independent of each other.

• Probability that an item j belongs to class i, given a set of attribitutes:

• P(Ci | A1=v1 & A2=v2 …An=vn)• If all A’s independent, we can use:• P(Ci) = P(A = Vj | Ci)

– (this is easy to compute)

• Pick the C with the highest probability.

Training a Naïve Bayes Classifier

• How do we know P(A = vj | Ci)?

• User labels data for us (says what she likes).

• For each class, we compute the fraction of times that A=vj

Example

• Two classes (yes, no)• Three documents, each of which have four words.• D1: {cat, dog, fly, cow} -> yes• D2: {crow, straw, fly, zebra} -> no• D3: {cat, dog, zoom, flex} -> yes• Number of unique words in ‘yes’: 6• Number of unique words in ‘no’: 4• Total # of words: 9

Example

• P(cat | yes): 2/6

• P(cat | no): 0/6

• P(yes | {cat, zoom, fly, dog}) =

2/6 * 1/6 * 1/6 * 2/6 = 0.003

• P(no | {cat, zoom, fly, dog}) =

* * 1/4 * ~ 0.00025

(epsilon helps us deal with sparse data)

Rule-learning algorithms

• If data is structured, rules can be learned for classification– Director=kubrick && star=mcdowell -> like– Title=“police academy*” -> not like

• These rules can be stored efficiently as a decision tree– Tests at each node.

• Fast, easy to learn, can handle noise

Decision TreesTitle=Police Academy

yes no

Not like Director=kubrick

Star=mcdowell

yes

yes no

no

…

…like

Other model-learning approaches

• TFIDF– Produces similar results to Naïve Bayes

• Neural Net– Learns a nonlinear function mapping features to

classes.– More powerful, but results can be hard to

interpret.

Comparing users to users

• Often, it’s easier to compare users to other users.– Less data needed– No knowledge of items required.

• Typical approach involves nearest-neighbor classification.

Nearest-neighbor classification

• We create a feature vector for each user containing an element for each ratable item.

• To compare two users, we compute the Euclidean distance between the ‘filled-in’ elements of their feature vectors.

• Sqrt(i(|uji – uki)2) • To recommend, find a similar user, then

find things that user rated highly.

Example

• Say our domain consists of four movies:– Police Academy– Clockwork Orange– Lord of the Rings– Titanic

• We represent this as a four-tuple:– <r1, r2, r3, r4>

Example

• We currently have three users in the system– u1: <10, 3, 9, ->– u2: <-, 9, 6, 2>– u3: <1, 7, -, 3>

• A new user u4, comes in. – <9, -,-,->

• Most similar to u1, so we would recommend they see Lord of the Rings and avoid Clockwork Orange

Personal and Ethical Issues

• How to get users to reveal their preferences?

• How to get users to rate all products equally (not just ones they love or hate)

• Users may be reluctant to give away personal data.

• Users may be upset by “preferential” treatment.

Summary

• Recommender systems allow online retailers to customize their sites to meet consumer tastes.– Aid browsing, suggest related items.

• Personaliztion is one of e-commerce’s advantages compared to brick-and-mortar stores.

• Challenges: obtaining and mining data, making intelligent and novel recommendations, ethics.

• Can perform comparisons across users or across items.– Trade off data needed versus detail of recommendation.