Recommender Systems

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Recommender Systems
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Recommender Systems

• In many cases, users are faced with a wealth of
  products and information from which they can
  choose.
• To alleviate this many web sites help users by
  using Recommender Systems,
• List of items or page that are likely to interest
  them
• Once the user makes a choice, a new list can be
  presented

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What Data is used to make the recommendations?

• Explicit feedback
• Ratings
• Reviews
• Auctions
• Implicit feedback
• Page visits
• Purchase data
• Browsing paths

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What are the type of recommendations?

• Item-to-Item associations
• Similar pages this
• Users who bought this book also bought X
• User-to-User associations
• Which other user has similar interests?
• User-to-Item associations
• Rating history describes user
• Items are described by attributes
• Items are described by ratings of other users

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Classification of Recommender Systems

• Content-based approach
• Item is described by a set of attributes
• Movies e.g director, genre, year, actors
• Documents bag-of-words
• Similarity metric defines relationship between
  items
• e.g. cosine similarity
• Examples
• related pages in search engine
• Google News

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Related Approaches

• Mooney and Roy (2000)
• Their approach comes from the Information
  Retrieval (IR) field
• They rely on the content of the items, and use
  some similarity score to match the items based on
  their content
• Burke (2000)
• The use the content-based recommendation.
• However, they allow to the user introduce
  explicit information about his preferences.

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Types of Recommender Systems

• Collaborative filtering
• Item is described by user interactions
• Matrix V of n (number of users) rows and m
  (number of items) columns
• Elements of matrix V are the user feedback
• Examples
• Rating given to item by each user
• Users who viewed this item
• Similarity metric between items

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Related Approaches

• Collaborative Filtering
• They used historical data gathered from other
  users to make the recommendation
• Ex If a user wants to rent a movie, he tends to
  rely on friends to recommend him items that they
  have like it
• The goal is to identify those users whose taste
  in recommendations is predictive of the taste of
  a certain person and use this recommendations to
  construct an interesting list for the user.

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Collaborative Filtering Models

• Memory Based
• Neighborhood Models
• Latent Factors
• Model Based
• Classification
• Bayesian Networks
• Association Rules

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Memory Based Approaches

• Works directly with the user data
• Given a user, the system finds the most similar
  users to make a recommendation
• There are two approaches
• Neighborhood
• Latent Factor

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Neighborhood Approach

• Its an item-oriented approach, focusing on
  evaluating the preference of a user to an item
  based on ratings of similar items by the same
  user.
• Users are transformed to item space by viewing
  them as baskets of rated items. No longer to
  compare users to items, but directly relate items
  to items.
• Pros rely on a few significant neighborhood
  relations effective at detecting very localized
  relationships
• Cons ignore the vast majority of ratings by a
  user unable to capture the totality of weak
  signals in all of a users rating.

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Latent Factor Models

• Transform both items and users to the same latent
  factor space, thus making them directly
  comparable.
• Latent space tries to explain ratings by
  characterizing both products and users on factors
  automatically inferred from user feedback.
• Pros effective at estimating overall structure
  that relates simultaneously to most or all items.
• Cons poor at detecting strong association among
  a small set of closely related items.

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Singular Value Decomposition

• Decompose ratings matrix, R, into coefficients
  matrix US and factors matrix V such thatis
  minimized.
• U eigenvectors of RRT (NxN)
• V eigenvectors of RTR (MxM)
• ? diag(?1,,?M) eigenvalues of RRT

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Challenges Collaborative Filtering

• User Cold-Start problemnot enough known about
  new user to decide who is similar (and perhaps no
  other users yet..)

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Challenges Collaborative Filtering

• Sparsity when recommending from a large item
  set, users will have rated only some of the
  items(makes it hard to find similar users)

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Challenges Collaborative Filtering

• Scalabilitywith millions of users and items,
  computations become slow
• Item Cold-Start problemCannot predict ratings
  for new item till some similar users have rated
  it No problem for content-based

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Related Approaches
Srebro Jaakkola (2003) Weighted SVD

• Binary weights
• wij 1 means element is observed
• wij 0 means element is missing
• Positive weights
• weights are inversely proportional to noise
  variance
• allow for sampling density e.g. elements are
  actually sample averages from counties or
  districts

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Related Approaches

• SVD with Missing Values
• Uses Expectation maximization to calculate the
  approximation of matrix
• E step fills in missing values of ranking matrix
  with the low-rank approximation matrix
• M step computes best approximation matrix in
  Frobenius norm
• Local minima exist for weighted SVD

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Related Approaches

• Agarwal (2009) Regression-Based Latent Factor
  Models
• They presented a regression based factor model
  that regularizes and deals with both cold-start
  and warm-start in a single framework.

It takes advantage of other user ratings, item
and user features to predict the missing ratings
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Model Based Approaches

• User data is compressed into a predictive model
• Instead of using ratings directly,develop a
  model of user ratings
• Use the model to predict ratings for new items
• To build the model
• Bayesian network (probabilistic)
• Clustering (classification)
• Rule-based approaches (e.g., association rules
  between co-purchased items)

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Related Approaches

• Stern(2009)
• Large Scale Online Bayesian Recommender
• Integrates Collaborative Filtering with Content
  information.
• Users and items compared in the same space.
• Flexible feedback model.
• Bayesian probabilistic approach.

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Value of the Recommendation

• Many considerations are taken into account to
  build the list of recommendations
• The likelihood of a recommendation to been
  accepted by the user
• The immediate value to the site
• The long term implications of the recommendations
  on the users future choices

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Value of the Recommendation

• Example
• Suggest a video camera with probability 0.5 or a
  VCR with a probability 0.6
• To recommend the video camera is less profitable
  than the VCR
• It the long term it might be more profitable (the
  camera has accessories that are likely to be
  purchased whereas the VCR does not)

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Sequential Nature of Recommendation Process
The recommender system suggests items to the user
The user can accept or not one the items offered
A new list of items is calculated based on the
user past ratings
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Markov Decision Process (MDP)

• A MDP is a model for stochastic decision problems
• A MDP is a four-tuple (S,A,Rwd, tr) where S is a
  set of states, A is a set of actions, Rwd is the
  reward associated with each state/action and tr
  is the transition function for each state.
• The goal is to behave in order to maximize the
  total reward
• The optimal solution p is a policy specifying
  which action to perform in each state .

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Markov Decision Process (MDP)

• The value function V of the policy p is defined
  as
• Where ? is a discount factor
• And the optimal value function V is defined as

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Markov Decision Process (MDP)

• To find the optimal policy p and its
  corresponding value function V
• We search the space of the possible policies
  starting with an initial policy p0(s)
• At each step we compute the value function based
  on the former policy and update the policy based
  on the new value function

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Temporal Dynamics in the Recommendations

• Item-side effects
• Product perception and popularity are constantly
  changing
• Seasonal patterns influence items popularity
• User-side effects
• Customers ever redefine their taste
• Transient, short-term bias anchoring
• Drifting rating scale
• Change of rater within household

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Temporal dynamics - challenges

• Multiple sources Both items and users are
  changing over time
• Multiple targets Each user/item forms a unique
  time series ? Scarce data per target
• Inter-related targets Signal needs to be shared
  among users foundation of collaborative
  filtering ? cannot isolate multiple problems

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Time Sensitive Recommenders

• Koren (2009)
• Collaborative Filtering with Temporal Dynamics
• He use factor models to separate different
  aspects of the ratings to observe changes in
• Rating scale of individual users
• Popularity of individual items
• User preferences

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Recommender Systems with Social Networks

• Use the interaction of the user with others to do
  recommendations
• Motivation
• Social Influence users adopt the behavior of
  their friends
• Challenges
• How do we define influence between users?

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Recommender Systems with Social Networks

• Preliminary Approaches
• Jamali Ester (2009)
• TrustWalker A Random Walk Model for Combining
  Trust-based and Item-based Recommendation
• Explores the trust network to find Raters.
• Aggregate the ratings from raters for prediction.
• Different weights for users

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Open Challenges

• Transparency
• Convince a user to accept a recommendation
• Help a user make a good decision
• Help a user fit a goal or mood
• Exploration versus Exploitation
• Cold start problems (for new items, and for new
  users)
• Choosing what questions to ask users
• Trade-off between optimizing for this user vs.
  for all users
• How can meta-data on user or item help?
• Guided Navigation
• Providing a guide over a vast body of content
• User's intent detection

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Open Challenges

• Time Value
• Does value of user input decay with time?
• Do items change in relevance with time?
• How to adjust for recent user experience?
• Evaluation of the recommenders performance
• Scalability
• Combining Content and Collaborative Recommenders
  efficiently