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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

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customization
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
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
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.
examples1
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.
examples2
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
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 recommendations1
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 recommendations2
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 recommendations3
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 recommendations4
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 recommendations5
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 recommendations6
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
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
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
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
na ve bayes classifier
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 P(A = Vj | Ci)
    • (this is easy to compute)
  • Pick the C with the highest probability.
training a na ve bayes classifier
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
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
example1
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}) =

e * e * 1/4 * e ~ 0.00025

(epsilon helps us deal with sparse data)

rule learning algorithms
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 trees
Decision Trees

Title=Police Academy

yes

no

Not like

Director=kubrick

yes

no

Star=mcdowell

yes

no

like

other model learning approaches
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
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
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(Si(|uji – uki)2)
  • To recommend, find a similar user, then find things that user rated highly.
example2
Example
  • Say our domain consists of four movies:
    • Police Academy
    • Clockwork Orange
    • Lord of the Rings
    • Titanic
  • We represent this as a four-tuple:
example3
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
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
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.
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