1. Recommender Systems

2. 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? )

3. 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.

4. 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.

5. 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.

6. 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

7. 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.

8. 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.

9. 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.

10. 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.

11. 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 …”

12. 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.

13. 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

14. 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.

15. 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.

16. 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

17. 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(C i | A 1 =v 1 & A 2 =v 2 …A n =v n ) If all A’s independent, we can use: P(C i ) =  P(A = V j | C i ) –(this is easy to compute) Pick the C with the highest probability.

18. Training a Naïve Bayes Classifier How do we know P(A = v j | C i )? User labels data for us (says what she likes). For each class, we compute the fraction of times that A=v j

19. 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

20. 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)

21. 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

22. Decision Trees Title=Police Academy yes no Not like Director=kubrick Star=mcdowell yes no … … like

23. 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.

24. 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.

25. 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 (|u ji – u ki ) 2 ) To recommend, find a similar user, then find things that user rated highly.

26. Example Say our domain consists of four movies: –Police Academy –Clockwork Orange –Lord of the Rings –Titanic We represent this as a four-tuple: –

27. Example We currently have three users in the system –u 1 : –u 2 : –u 3 : A new user u 4, comes in. – Most similar to u 1, so we would recommend they see Lord of the Rings and avoid Clockwork Orange

28. 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.

29. 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.