Introduction to Recommender Systems

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Introduction to Recommender Systems

• Adapted from
• FRANCESCO RICCI
• eCommerce and Tourism Research Laboratory
• Automated Reasoning Systems Division
• ITC-irst
• Trento Italy
• ricci_at_itc.it

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What movie should I see?
The Internet Movie Database (IMDb) provides
information about actors, films, television
shows, television stars, video games and
production crew personnel. Owned by Amazon.com
since 1998, as of June 21, 2006 IMDb featured
796,328 titles and 2,127,371 people.
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What travel should I do ?

• I would like to escape from this ugly an tedious
  work life and relax for two weeks in a sunny
  place. I am fed up with these crowded and noisy
  places just the sand and the sea and some
  adventure.

• I would like to bring my wife and my children on
  a holiday it should not be to expensive. I
  prefer mountainous places not to far from home.
  Children parks, easy paths and good cuisine are a
  must.

• I want to experience the contact with a
  completely different culture. I would like to be
  fascinated by the people and learn to look at my
  life in a totally different way.

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What book should I buy?
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What news should I read?
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What paper should I read ?
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A Solution
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Information Overload

• Internet information overload, i.e., the state
  of having too much information to make a decision
  or remain informed about a topic
• Information retrieval technologies can assist a
  user to locate content if the user knows exactly
  what he is looking for (with some difficulties!)
• The user must be able to say yes this is what I
  need when presented with the right result
• But in many information search task, e.g.,
  product selection, the user is
• not aware of the range of available options
• may not know what to search
• if presented with some results may not be able to
  choose.

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Decisions

• When there are 100.000.000 of options it is
  obvious we need tools for searching, filtering,
  ranking options
• But even when there are a few dozen of options we
  need support
• Examples
• Where to go for dinner tonight?
• What flight for going to London?
• What Digital SLR camera should I buy?

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Non-personalized tools

• A printed catalogue of products, e.g., of books
  or clothes or travels
• A shop window
• A car exhibition in a cars shop
• A movie finder data base (search by title or
  actor)
• A Podcast directory

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Personalized tools

• A printed catalogue of products, e.g., of books
  or clothes or travels
• THAT SHOWS ONLY THE PRODUCTS THAT YOULL LIKE TO
  HAVE
• A shop window
• THAT SHOWS IN THE BEST PLACES THE PRODUCTS YOURE
  SEARCHING
• A car exhibition in a cars shop
• THAT HAVE EXACTLY THE MODELS YOU THOUGH TO BUY
• A movie finder data base (search by title or
  actor)
• THAT DOES NOT SHOW THE MOVIES YOU HATE
• A Podcast directory
• THAT LISTS ONLY PODCASTS YOU LIKE

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Personalization

• Personalization is the ability to provide
  content and services tailored to individuals
  based on knowledge about their preferences and
  behavior
• Personalization is the capability to customize
  customer communication based on knowledge
  preferences and behaviors at the time of
  interaction with the customer
• Personalization is about building customer
  loyalty by building a meaningful one-to-one
  relationship by understanding the needs of each
  individual and helping satisfy a goal that
  efficiently and knowledgeably addresses each
  individuals need in a given context

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Personalization Goals

• Provide the users with what they want or need
  without requiring them to ask explicitly
• Not a fully automated process
• Different approaches require different levels of
  user involvement and human computer interactivity
• Personalization techniques try to leverage all
  available information about users to deliver a
  personal experience

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Personalization

• Context
• pre-travel vs. during travel
• on-the-net, on-the-move, on-the-tour
• traveling, wandering and visiting
• environment in the hotel, train, car, airplane,
  at the conference
• Time/Space coordinates
• Business vs. Fun

User Knowledge, experience, budget, travel party,
cognitive capabilities, motivations (security,
variety, fun, etc.), age, language, disabilities

• Need
• Buy a complete travel package,
• choose a restaurant,
• collect information on a location,
• find the route,
• find the transportation mean
• communicate with other travelers

Device/Capabilities Personal computer, PDA, smart
phone, phone payment, movies, broadcast,
instant messaging, email, position, etc.
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Suppliers Motivations

• Making interactions faster and easier.
  Personalization increases usability, i.e., how
  well a web site allows people to achieve their
  goals.
• Increasing customer loyalty. A user should be
  loyal to a web site which, when is visited,
  recognizes the old customer and treats him as a
  valuable visitor.
• Increasing likelihood of repeated visits. The
  longer the user interacts with the site, the
  more refined his user model maintained by the
  system becomes, and the more the web site can be
  effectively customized to match user
  preferences.
• Maximize look-to-buy ratio. It turns out to be
  look-to-book ratio in the travel and tourism
  industry, which is actually the essential
  indicator of personalization objectives in this
  domain.

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Recommender Systems

• A recommender system helps to make choices
  without sufficient personal experience of the
  alternatives
• To suggest products to their customers
• To provide consumers with information to help
  them decide which products to purchase
• They are based on a number of technologies
  information filtering, machine learning, adaptive
  and personalized system, user modeling,

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Examples

• Some examples found in the Web
• Amazon.com looks in the user past buying
  history, and recommends product bought by a user
  with similar buying behavior
• Tripadvisor.com - Quoting product reviews of a
  community of users
• Activebuyersguide.com make questions about
  searched benefits to reduce the number of
  candidate products
• Trip.com make questions and exploits to
  constraint the search (exploit standardized
  profiles)
• Smarter Kids self selection of a user profile
  classification of products in user profiles

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Core Recommendation Techniques
U is a set of users I is a set of items/products
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Evaluating Recommender Systems

• The majority focused on systems accuracy in
  supporting the find good items users task
• Assumption if a user could examine all items
  available, he could place them in a ordering of
  preference
• Measure how well is the system in predicting the
  exact rating value (value comparison)
• Measure how well the system can predict whether
  the item is relevant or not (relevant vs. not
  relevant)
• Measure how close the predicted ranking of items
  is to the users true ranking (ordering
  comparison)

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How Has Been Measured

• Split the available data (so you need to collect
  data first!), i.e., the user-item ratings into
  two sets training and test
• Build a model on the training data
• For instance, in a nearest neighbor
  (memory-based) CF simply put the ratings in the
  training in a separate set
• Compare the predicted rating on each test item
  (user-item combination) with the actual rating
  stored in the test set
• You need a metric to compare the predicted and
  true rating

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Accuracy Comparing Values

• Measure how close the recommender systems
  predicted ratings are to the true user ratings
  (for all the ratings in the test set).
• Predictive accuracy (rating) Mean Absolute Error
  (MAE), pi is the predicted rating and ri is the
  true one
• Variation 1 Mean Squared Error (take the square
  of the differences), root mean squared error (and
  then take the square root). These emphasize large
  errors.
• Variation 2 Normalized MAE MAE divided by the
  range of possible ratings allowing comparing
  results on different data sets, having different
  rating scales.

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Precision and Recall

• Precision is the ratio of relevant items selected
  by the recommender to the number of items
  selected (Nrs/Ns)
• Recall is the ratio of relevant items selected to
  the number of relevant (Nrs/Nr)
• Precision and recall are the most popular metrics
  for evaluating information retrieval systems.

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Precision and Recall Example

• We assume to know the relevance of all the items
  in the catalogue for a given user
• The orange portion is that recommended by the
  system

P4/70.57 R4/90.44
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Rank Accuracy Metrics

• Rank is the position in the sorted list
• Spearmans rank correlation (ui and vi are the
  ranks of item i in the user and system order)
• Kendalls Tau (C is the number of concordant
  pairs pairs in the same order in both ranked
  lists D is the number of discordant pairs, TR
  tied pairs (same ranking) in the user order, TP
  tied pair in the predicted order

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Core Recommendation Techniques
U is a set of users I is a set of items/products
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Movie Lens
http//movielens.umn.edu
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The CF Ingredients

• List of m Users and a list of n Items
• Each user has a list of items he/she expressed
  their opinion about (can be a null set)
• Explicit opinion - a rating score (numerical
  scale)
• Sometime the rating is implicitly purchase
  records
• Active user for whom the CF prediction task is
  performed
• A metric for measuring similarity between users
• A method for selecting a subset of neighbors for
  prediction
• A method for predicting a rating for items not
  currently rated by the active user.

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Collaborative-Based Filtering

• The collaborative based filtering recommendation
  techniques proceeds in these steps
• For a target/active user (the user to whom a
  recommendation has to be produced) the set of his
  ratings is identified
• The users more similar to the target/active user
  (according to a similarity function) are
  identified (neighborhood formation)
• The products bought by these similar users are
  identified
• For each one of these products a prediction - of
  the rating that would be given by the target user
  to the product - is generated
• Based on this predicted rating a set of top N
  products are recommended.

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Nearest Neighbor Collaborative-Based Filtering
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User Model interaction history
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Collaborative-Based Filtering

• A collection of user ui, i1, n and a collection
  of products pj, j1, , m
• A n m matrix of ratings vij , with vij ? if
  user i did not rate product j
• Prediction for user i and product j is computed
  as

• Correlation factor can be computed by

• The sum (and average) are over j s.t. vij and vkj
  are not ?

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Collaborative-Based Filtering

• Pros require minimal knowledge engineering
  efforts (knowledge poor)
• Users and products are symbols without any
  internal structure or characteristics
• Cons
• Requires a large number of explicit and reliable
  rates to bootstrap
• Requires products to be standardized (users
  should have bought exactly the same product)
• Assumes that prior behavior determines current
  behavior without taking into account contextual
  knowledge (session-level)
• Does not provide information about products or
  explanations for the recommendations
• Does not support sequential decision making or
  recommendation of good bundling, e.g., a travel
  package.

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Problems of CF Sparsity

• Typically we have large product sets and user
  ratings for a small percentage of them
• Example Amazon millions of books and a user may
  have bought hundreds of books
• the probability that two users that have bought
  100 books have a common book (in a catalogue of 1
  million books) is 0.01 (with 50 and 10 millions
  is 0.0002).
• We must have a number of users comparable to one
  tenth of the size of the product catalogue

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Problems of CF Scalability

• Nearest neighbor algorithms require computations
  that grows with both the number of customers and
  products
• With millions of customers and products a
  web-based recommender will suffer serious
  scalability problems
• The worst case complexity is O(mn) (m customers
  and n products)
• But in practice the complexity is O(m n) since
  for each customer only a small number of products
  are considered (one loop on the customers to
  compute similarity and one on the products to
  compute the prediction)

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Personalised vs Non-Personalised CF

• Collaborative-based recommendations are
  personalized since the prediction is based on
  the ratings (for a given item) expressed by
  similar users
• A non-personalized collaborative-based
  recommendation can be generated by averaging the
  recommendations of ALL the users
• How the two approaches would compare?

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Personalised vs Non-Personalised CF
Not much difference indeed!
vij is the rating of user i for product j and vj
is the average rating for product j
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Core Recommendation Techniques
U is a set of users I is a set of items/products
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Content-Based Recommendation

• In content-based recommendations, the system
  tries to recommend items similar to those a given
  user has liked in the past
• In contrast with collaborative recommendation
  where the system identifies users whose tastes
  are similar to those of the given user and
  recommends items they have liked
• A pure content-based recommender system makes
  recommendations for a user based solely on the
  profile built up by analyzing the content of
  items which that user has rated in the past.

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Content-Based Recommender

• It is mainly used for recommending text-based
  products (web pages, usenet news messages, )
• The items to recommend are described by their
  associated features (e.g. keywords)
• The User Model can be structured in a similar
  way as the content for instance the
  features/keywords more likely to occur in the
  preferred documents (lazy approach)
• Then, text documents can be recommended based on
  a comparison between their content (words
  appearing in the text) and a user model (a set of
  preferred words)
• The user model can also be a classifier based on
  whatever technique (Neural Networks, Naïve Bayes,
  C4.5, )

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Syskill Webert

• Assisting a person to find information that
  satisfies long-term, recurring goals (e.g.
  digital photography)
• Feedbacks on the interestingness of a set of
  previously visited sites is used to learn a
  profile
• The profile is used to predict interestingness of
  unseen sites.

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Supported Interaction

• The user identifies a topic (e.g. Biomedical) and
  a page with many links to other pages on the
  selected topic (index page)
• The user can then explore the Web with a browser
  that in addition to showing a page
• Offers a tool for collecting user ratings on
  displayed pages
• Suggests which links on the current page are
  (estimated) interesting.

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Syskill Webert User Interface
The user indicated interest in
The user indicated no interest in
System Prediction
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Learning

• A document (HTML page) is described as a set of
  Boolean features (a word is present or not)
• They used a Bayesian classifier (for each user),
  where the probability that a document w1v1, ,
  wnvn (e.g. car1, story0, , price1) belongs
  to a class (cold or hot) is
• Both P(wj vjChot) (i.e., the probability that
  in the set of the documents liked by a user the
  word wjis present or not) and P(Chot) is
  estimated from the training data
• After training on 30/40 examples it can predict
  hot/cold with an accuracy between 70 and 80

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Problems of Content-Based Recommenders

• A very shallow analysis of certain kinds of
  content can be supplied
• Some kind of items are not amenable to any
  feature extraction methods with current
  technologies (e.g. movies, music)
• Even for texts (as Web pages), the IR techniques
  cannot consider multimedia information, aesthetic
  qualities, download time
• Hence if you rate positively a page it could be
  not related to the presence of certain keywords!

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Problems of Content-Based Recommenders

• Over-specialization the system can only
  recommend items scoring high against a users
  profile the user is recommended with items
  similar to those already rated
• Requires user feed-backs the pure content-based
  approach (similarly to CF) requires user feedback
  on items in order to provide meaningful
  recommendations
• It tends to recommend expected items this tends
  to increase trust but could make the
  recommendation not much useful (no serendipity)
• Works better in those situations where the
  products are generated dynamically (news,
  email, events, etc.) and there is the need to
  check if these items are relevant or not.

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Core Recommendation Techniques
U is a set of users I is a set of items/products
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Demographic Methods

• Aim to categorize the user based on personal
  attributes and make recommendation based on
  demographic classes
• Demographic groups can come from marketing
  research hence experts decided how to model the
  users
• Demographic techniques form people-to-people
  correlations

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Demographic-based personalization
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Demographic-based personalization
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Demographic Methods (more sophisticated)

• Demographic features in general are asked
• But can also induced by classifying a user using
  other user descriptions (e.g. the home page)
  you need some user for which you know the class
  (e.g. male/female)
• Prediction can use whatever learning mechanism we
  like (nearest neighbor, naïve classifier, etc.)

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Core Recommendation Techniques
U is a set of users I is a set of items/products
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Utility methods

• A utility function is a map from a state onto a
  real number, which describes the associated
  degree of happiness
• Can build a long term utility function but more
  often the systems using such an approach try to
  acquire a short term utility function
• They must acquire the user utility function, or
  the parameters defining such a function

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Utility related information
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Utility

• The item is described by a list of attributes
  (numerical) p1, pm, e.g., number of rooms,
  square meters, levels, (MaxCost Cost),
• It is generally assumed that higher values of the
  attribute correspond to higher utilities
• The user is modeled with a set of weights, u1, ,
  um (in 0,1) on the same attributes
• The objective is to find (retrieve) the products
  with larger utility (maximal)
• The problem is the elicitation or learning of
  user model u1, , um

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Utility and similarity

• If the user has some preferred values for the
  attributes, e.g, q1, , qm, one can substitute
  to the value of the product a local similarity
  function sim(qj, pj)
• A typical local similarity function is (1-qj
  pj/rangei), where rangei is the difference
  between the max and min value of the attribute i.
• A utility-based recommender become a similarity
  maximization recommender (or a nearest neighbor
  recommender).

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Core Recommendation Techniques
U is a set of users I is a set of items/products
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Knowledge Based Recommender

• Suggests products based on inferences about a
  users needs and preferences
• Functional knowledge about how a particular item
  meets a particular user need
• The user model can be any knowledge structure
  that supports this inference
• A query
• A case (in a case-based reasoning system)
• An adapted similarity metric (for matching)
• A part of an ontology
• There is a large use of domain knowledge encoded
  in a knowledge representation language/approach.

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Hybrid Methods

• Try to address the shortcomings of several
  approaches, and produce recommendations using a
  combination of those techniques
• There is a large variability on these hybrid
  methods there is no standard hybrid method
• We shall present some of them here but many will
  come later

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Fab System

• The user profile is based on content analysis
• Two user profiles are compared to determine
  similar users
• Then a collaborative based recommendation is
  generated
• User receive items both when they score highly
  against their own profile and when rated highly
  by a user with a similar profile
• This is a mixed approach

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Fab Architecture

• Collection agents find pages relevant for a
  specific topic
• Selection agent find pages for a specific user
• Central router forwards pages on to those users
  whose profiles they match above some threshold.

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User Feedback

• When the user has requested, received, and looked
  over the recommendations, they are required to
  assign a rating (7-point scale)
• Ratings are store in the personal agent profile
• Ratings are sent to the collection agents for
  adapting the user profiles (stored by him)
• Highly rated pages are sent to similar users
  (collaborative)

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Collaboration via Content

• Problem addressed in a collaborative-based
  recommender, products rated by pair of users may
  be very few correlation between two users is
  not reliable
• In collaboration via content the content-based
  profile of each user is exploited to detect
  similarities among users.

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Comparison

• Contentbased recommendation is done with Bayes
  classifier
• Collaborative is standard using Pearson
  correlation
• Collaboration via content uses the content-based
  user profiles

Averaged on 44 users Precision is computed in the
top 3 recommendations ( of plus in the
recommendation list)/3
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Hybridization Methods
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Weighted

• The score of a recommended item is computed from
  the results of all of the available
  recommendation techniques present in the system
• Example 1 a linear combination of recommendation
  scores
• Example 2 treats the output of each recommender
  (collaborative, content-based and demographic) as
  a set of votes, which are then combined in a
  consensus scheme
• The implicit assumption in this technique is that
  the relative value of the different techniques is
  more or less uniform across the space of possible
  items
• Not true in general e.g. a collaborative
  recommender will be weaker for those items with a
  small number of raters.

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Switching

• The system uses some criterion to switch between
  recommendation techniques
• Example The DailyLearner system uses a
  content-collaborative hybrid in which a
  content-based recommendation method is employed
  first
• If the content-based system cannot make a
  recommendation with sufficient confidence, then a
  collaborative recommendation is attempted
• This switching hybrid does not completely avoid
  the ramp-up problem, since both the collaborative
  and the content-based systems have the new user
  problem
• The main problem of this technique is to identify
  a GOOD switching condition.

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Mixed

• Recommendations from more than one technique are
  presented together
• The mixed hybrid avoids the new item start-up
  problem
• It does not get around the new user start-up
  problem, since both the content and collaborative
  methods need some data about user preferences to
  start up.

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Feature Combination

• Achieves the content-collaborative merger
  treating collaborative information (ratings of
  users) as simply additional feature data
  associated with each example and use
  content-based techniques over this augmented data
  set
• The feature combination hybrid lets the system
  consider collaborative data without relying on it
  exclusively, so it reduces the sensitivity of the
  system to the number of users who have rated an
  item
• The system have information about the inherent
  similarity of items that are otherwise opaque to
  a collaborative system.

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Cascade

• One recommendation technique is employed first to
  produce a coarse ranking of candidates and a
  second technique refines the recommendation from
  among the candidate set
• Example EntreeC uses its knowledge of
  restaurants to make recommendations based on the
  users stated interests. The recommendations are
  placed in buckets of equal preference, and the
  collaborative technique is employed to break ties
• Cascading allows the system to avoid employing
  the second, lower-priority, technique on items
  that are already well-differentiated by the first
• But requires a meaningful and constant ordering
  of the techniques.

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Feature Augmentation

• Produce a rating or classification of an item and
  that information is then incorporated into the
  processing of the next recommendation technique
• Example Libra system makes content-based
  recommendations of books based on data found in
  Amazon.com, using a naive Bayes text classifier
• In the text data used by the system is included
  related authors and related titles
  information that Amazon generates using its
  internal collaborative systems
• Very similar to the feature combination method
• Here the output of a recommender system is used
  for a second RS
• In feature combination the representations used
  by two systems are combined.

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Meta-Level

• Using the model generated by one as the input for
  another
• Example FAB
• user-specific selection agents perform
  content-based filtering using Rocchios method to
  maintain a term vector model that describes the
  users area of interest
• Collection agents, which gather new pages from
  the web, use the models from all users in their
  gathering operations.
• Documents are first collected on the basis of
  their interest to the community as a whole and
  then distributed to particular users
• Example collaboration via content the model
  generated by the content-based approach is used
  for representing the users in a collaborative
  filtering approach.

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Summary

• Collaborative-based method is one of the most
  popular
• Suffers from bootstrapping problems
• Content-based methods are well rooted in
  information retrieval
• Demographic methods are very simple and could
  provide limited personalization (sometime can be
  sufficient)
• Utility-based methods go to the root of the
  decision problem how to acquire the utility
  function?
• Hybrid methods are the most powerful and popular
  right now there are plenty of options for
  hybridization

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New User Problem

• Personalization systems can work only if some
  information about the target user is available
• No User Model No personalization
• If the required user model data are not available
  then try to arrive to them with a shortcut
• Web of trust to identify similar users in a CF
  system
• Mining publications of a user and its similar
  users help in defining the topics that are
  interesting for a target user
• The output is always a sort of similarity
  function!!!

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New Item Problem

• Not much a problem for content-based filtering
  it is the main advantage of content-based
  filtering
• Critical problem for user-user and item-item
  collaborative filtering
• Solution exploit similar items
• Example NutKing/Dietorecs ranking based on
  double similarity an implicit rating for a
  similar item is considered to be assigned to the
  target item

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Recommendation and User Context

• All RS should adapt to the context of search of
  the user but some methods cannot cope with that
  easily (e.g. CF)
• It depends on the definition of context but in
  practice this includes
• Short term preferences (tomorrow I want )
• Information related to the specific space-time
  position of the user
• Motivations of the search (a present to my
  wife)
• Circumstances (Ive some time to spend here)
• Emotions (I feel adventure)
• Availability of data

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Privacy

• Recommender systems are based on user information
• There are laws that impose restrictions on the
  usage and distribution of information about
  people
• RS must cope with these limitations e.g.
  distributed recommender systems exchanging user
  profiles could be impossible for legal reasons!
• RS must be developed in such a way to limit the
  possibility that an attacker could learn personal
  data on some users
• There is the need to develop techniques that
  limit the number and type of personal data used
  in a RS.

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Robusteness

• The recommender should be robust against attacks
  aiming at modifying the system such that it will
  recommend a product more often than others
• Shilling
• Nuking
• Some algorithms may be more robust than others
• Content based methods are not influenced at all
  by false ratings

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Challenges

• Generic user models (multiple products and tasks)
• Generic recommender systems (multiple products
  and tasks)
• Distributed recommender system (users and
  products data are distributed)
• Portable recommender systems (user data stored at
  user side)
• (user) Configurable recommender systems
• Multi strategy adapted to the user
• Privacy protecting RS
• Context dependent RS
• Emotional and values aware RS
• Trust and recommendations
• Persuasion technologies
• Easily deployable RS
• Group recommendations

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Challenges

• Interactive Recommendations sequential decision
  making
• Hybrid recommendation technologies
• Consumer Behavior and Recommender Systems
• Complex Products recommendations
• Mobile Recommendations
• Business Models for Recommender Systems
• High risk and value recommender systems
• Recommendation and negotiation
• Recommendation and information search
• Recommendation and configuration
• Listening customers
• Recommender systems and ontologies

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Thank you for your patience!
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Trip.com
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Trip.com
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Trip.com
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