Recommender Systems

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

What Data is used to make the recommendations?

Explicit feedback Ratings Reviews Auctions

• Implicit feedback Page visits Purchase data Browsing paths

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 associationsRating history describes user Items are described by attributes Items are described by ratings of other users

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

Related Approaches

Mooney and Roy (2000)Their approach comes from the Information

Retrieval (IR) fieldThey 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.

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

Related Approaches

Collaborative FilteringThey 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.

Collaborative Filtering Models

Memory BasedNeighborhood ModelsLatent Factors

Model BasedClassificationBayesian NetworksAssociation Rules

Memory Based Approaches

Works directly with the user dataGiven a user, the system finds the most

similar users to make a recommendation

There are two approaches: Neighborhood Latent Factor

Neighborhood Approach

It’s 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 user’s rating.

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 U

and factors matrix V such that

is minimized. U = eigenvectors of RRT (NxN) V = eigenvectors of RTR (MxM) = diag(1,…,M) eigenvalues of RRT

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

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

Challenges Collaborative Filtering

Challenges Collaborative Filtering Scalability

with 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]

Related Approaches

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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Srebro & Jaakkola (2003)

Weighted SVD

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

Related ApproachesAgarwal (2009)

Regression-Based Latent Factor ModelsThey 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

Model Based Approaches

User data is compressed into a predictive model Instead of using ratings directly,

develop a model of user ratingsUse the model to predict ratings for new itemsTo build the model:

Bayesian network (probabilistic) Clustering (classification) Rule-based approaches (e.g., association rules

between co-purchased items)

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.

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 siteThe long term implications of the

recommendations on the user’s future choices

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)

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

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 π is a policy specifying which action to perform in each state .

Markov Decision Process (MDP)

The value function V of the policy π is defined as:

Where γ is a discount factor

And the optimal value function V* is defined as:

Markov Decision Process (MDP)

To find the optimal policy π* and its corresponding value function V*: We search the space of the possible policies starting

with an initial policy π0(s)

At each step we compute the value function based on the former policy and update the policy based on the new value function

Temporal Dynamics in the Recommendations

Item-side effects:Product perception and popularity are constantly

changingSeasonal patterns influence items’ popularity

User-side effects:Customers ever redefine their tasteTransient, short-term bias; anchoringDrifting rating scaleChange of rater within household

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

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:

1. Rating scale of individual users

2. Popularity of individual items

3. User preferences

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?

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

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

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