Matches, Recommendations and the Netflix Challenge


Overview

 

A preview:

Our presentation will pull together material from various sources - see the references below. But most of it will come from [Bree1998], [Grif2000], [Herl1999], [Roth2008], and [Su2009].


Matching users to items: the marketplace

 

Consider an abstract market:

 

The standard approach: a supply-demand marketplace with prices

 

Market failures:

 

Let's examine an abstract market along two dimensions:


The stable marriage problem

 

First, let's describe the problem:

  • We will describe the simplest version of the problem:
    • n men: M1, M2, ..., Mn.
    • n women: W1, W2, ..., Wn.
    • Each man lists all women in some order of preference.
    • Each woman lists all men in some order of preference.
    • Broad goal: pair each man with a woman.

  • Thus, there is perfect clarity in buyer intentions, but limited resources
          → Each man is paired with one woman in the pairing.

  • We'll use the notation (Mi,Wj) to denote a couple produced by a pairing or marriage.

  • A marriage is stable if there is no Mi and Wj such that:
    • Mi and Wj are not married.
    • Mi is higher in Wj's list than her current spouse.
    • Wj is higher in Mi's list than his current spouse.

  • This is important because otherwise, Mi and Wj would not respect the marriage and would elope.

  • Example: the marriage below is not stable:

  • Thus, our goal is: compute a stable marriage if one exists.
 

The Proposal Algorithm:

  • Due to David Gale and Lloyd Shapely in 1962 [Gale1962].

  • Also called the Deferred-Acceptance Algorithm.

  • Key ideas: "man proposes, woman disposes"
    • Initially, all men and women are "free".
    • At each step, there is an engagement.
    • At each step, a free man proposes to any woman who has not rejected him before.
    • A woman is required to accept unless she's engaged to someone higher on her list.
    • If a woman is engaged to a lower-preference man, she "dumps" that man in favor of the proposer.
    • After no more engagements are broken and all are engaged, the marriage is finalized.

  • Note: the women always "trade up".

  • Let's work through an example:

    • M1 proposes to W2
            → She accepts

    • M2 proposes to W2
            → She breaks off with M1 and accepts M2

    • M1 is back on the free list.
    • M3 proposes to W1
            → She accepts

    • M1 proposes to W3
            → She accepts

    • Done.

    Exercise: Who gets the better deal - men or women?

  • In pseudocode:
    
    Algorithm: proposal (M, W)
    Input: n men M1,...,Mn and n women W1,...,Wn with their rankings
    1.    Add all men to freeList
    2.    while freeList is not empty
    3.        M = remove a man from freeList
    4.        W = highest-ranked woman he has not yet proposed to
    5.        if W is unengaged
    6.            M and W are engaged
    7.        else if W prefers M to her fiance M'
    8.            M and W are engaged
    9.            M' is placed on the free list
    10.       endif
    11.   endwhile
         
 

Does the algorithm work?

  • Does it end?
    • A is only rejected once by a woman.
    • This can happen only a finite number of times.

  • Is the marriage stable?

    • Suppose man M and woman W each prefer the other to their current spouses.
    • Suppose M is married to A and W is married to B.
    • M proposed to W before A.
    • Case 1: A rejected M's proposal.
            → She was already engaged to someone better than M.
            → But then, she couldn't be married to someone lower than M.
            → Contradiction!
    • Case 2: A dumped M.
            → She must have found someone better.
            → Contradiction!

  • Running time: O(n2)
    • At each step, an engagement occurs.
    • Each woman can only be engaged to n men.
            → Only O(n2) engagements possible.

  • A marriage is male-optimal if:
    • The marriage is stable.
    • Among all stable marriages, each male gets his top choice (among the choices in the stable marriages).

  • Similar definition for male-pessimal, female-optimal, and female-pessimal.

  • Result: the proposal algorithm results in a marriage that is male-optimal and female-pessimal.
 

History of the stable marriage problem:

  • Has its origins in hospital-resident matching:

  • 1900-1945: No regulation
          → No information, chaotic assignments

  • 1945-51: tight deadline
          → Bad for residents and second-choices (market unraveling)

  • 1952: Mullin-Stalnaker algorithm
          → Turned out to be unstable.

  • This was re-designed into the Boston-Pool algorithm that produced stable assignments.
          → Became the algorithm of choice for the National Resident Matching Program (NRMP).

  • 1984: Boston-Pool shown to be hospital-optimal.

  • 1998: Roth-Peranson algorithm used in NRMP
    • Handles couples.
    • Incomplete rank lists.
    • Limited seats for certain specialties.

  • 2004: Anti-trust lawsuit prompted enaction of a law to asserting its pro-competitiveness.
 

Other applications and developments:

  • All kinds of medical specialities.

  • New York and Boston schools.

  • Variations: stable-roomate problem, college admissions, blacklists.

  • Other studies: how to handle cheating.

  • If, with different numbers of men and women, a stable marriage is not possible, it is NP-hard to find the largest possible stable marriage.

  • Real marriages: see scientificmatch.com.


Graph Matching

 

A natural extension to the marriage problem is the matching problem:

  • A graph G=(V,E) is bipartite if the vertices V can be partitioned into two sets V = X U Y such that there is no edge between an X vertex and a Y vertex.

  • Example of a weighted bipartite graph:

  • A matching is a subset of edges such that no two edges share an endpoint.

  • Example of a matching:

  • Relationship to marriage problem:
    • Think of X as representing males, Y as females.
    • An edge weight is the "value" of a pairing.
    • Goal: find the maximum weight matching.

  • A further generalization: when G is non-bipartite, e.g.

 

Types of matching problems:

  • Unweighted bipartite matching
    • O(VE) augmenting path algorithm.
    • Improved to O(E√V) in 1973 by Hopcroft and Karp.

  • Weighted bipartite matching
    • O(V2E) so-called Hungarian algorithm.
    • Devised by Harold Kuhn in 1955.
    • Kuhn called it the Hungarian method because it was based on a few ideas by Hungarian mathematicians D.Konig and E.Egervary.
    • Can be improved to O(V2log(V) + VE) using a Fibonacci heap.

  • Unweighted general (non-bipartite) graph matching
    • Celebrated "blossoms" algrorithm by Jack Edmonds in 1965.
    • O(V2E) complexity.
    • Recall: Edmonds introduced the idea of polynomial vs. exponential algorithms.

  • Weighted general (non-bipartite) graph matching
    • Blossoms algorithm can be modified to work for weighted version.
    • Improvements by Gabow and collaborators to O(EVlog(V)) in 1986 and 1989.
    • An LP formulation can be solved with a polynomial number of constraints in O(V4) time.


Text matching (mining)

 

There are many versions of this problem, but let's start with the most elementary version:

  • We have a (large) collection of documents.

  • Each document is a "bag of words".

  • A user query is also a "bag of words".

  • The objective: return the most relevant documents to the user.

  • Note: this is a version of the marketplace problem
    • The user intention is "less clear".
    • But sellers (documents) have no resource limitations.

  • Here, words are the most basic unit:
    • However, one often uses word stems
            → e.g., "hatch" instead of "hatches" or "hatching".
    • Sometimes synonyms are used, but this has been found to have limited use.

  • There are variations of the basic problem that we won't consider:
    • Iterated query refinement, and question-answer frameworks.
    • Topic detection and classification.
    • Cross-language languages retrieval.

  • Each year the the TREC conference puts out challenge problems in various categories.
 

Three different approaches to text retrieval:

  • Boolean queries:
    • User specifies a query using Boolean operators such as:
            ("green" AND "eggs" AND "ham") OR ("Horton")
    • System finds all documents that exactly match the query.
    • Note: documents are not ranked
            → Either a document matches the query or not.
    • User studies show that only a few "power users" find this approach useful.
    • Relevant-ranked queries are the most preferred form of search.

  • Probabilistic-relevance approach:
    • Identify relevant documents.
    • For each relevant document, compute Pr[document is relevant].
    • Rank in order of probabilistic-relevance.

  • Vector-space model:
    • Treat each document as a vector in document-space.
    • A query is also a vector in the same space.
    • Find close matches and list in order of "closeness".
 

Let's look into the details of the latter two:


Data mining

 

What is data mining?

  • Example: Grocery store
    • Suppose a grocer examines past transaction data to conclude
            "When a customer buys wine (item i) they also buy specialty cheese (item j) with high probability"
    • Then, by placing cheese near the wine shelf, a grocer increases the chance of an impulse purchase.
    • Such associations can be extracted from transaction data.

  • Example: banking
    • Suppose a bank discovered that
            "77% of loan defaults involved a car loan to someone aged 18-21 for a red sportscar"
    • Then, such an inference from the data can drive policy.

  • Online shopping:
    • Amazon and others present "recommended items".
    • These are based on other customers' purchases.

  • This extraction of patterns from standard database data is called data mining.

  • History:
    • Late 1980's: rule extraction or knowledge discovery in databases.
    • An early landmark paper in 1993 [Agra1993] defined the association-rule problem which spurred the development of the field.

  • The association-rule problem:
    • The input: a database table of transaction data such as:
      
               Transaction #1:  eggs, milk, cookies
               Transaction #2:  beer, chips, pretzels, popcorn
               ...
               Transaction #i:  beer, chips, diapers
               ...
               Transaction #6,391,202: wine, brie, diapers
               
    • Goal: identify rules of the sort:
            "60% of the time diapers are purchased, beer and chips are also purchased".
    • The challenge: extract such rules efficiently from very large databases.

  • For more details: see these notes on data mining.
 

Amazon's recommendation engine:

  • For every pair of items, Amazon identifies a "co-purchase" score.

  • Algorithm:
    
    1.   Initialize purchase-pair-set S = φ
    2.   for each item i
    3.       for each customer c who purchased i
    4.           for each item j purchased by c
    5.               S = S U {(i,j)}
    6.           endfor
    7.       endfor
    8.   endfor
    
    9.   for each item-pair in φ
    10.         compute similarity-score for (x,y)
                // The score is the cosine distance between two items.
    11.      endfor
    12.  endfor
         

  • What is the similarity-score?
    • One simple way: the number of customers that bought both.
            → To compute this, add a counter after line 5 above.
    • Another way: compute the "cosine" distance treating items as column vectors in the matrix A where
            Aij = 1 if user i bought item j.

  • Note: Amazon's algorithm does not find similar users to make recommendations.


Collaborative filtering

 

What is collaborative filtering (CF)?

  • From a user's point of view:
          → "I'd like to know what users like me rate highly"

  • From a seller's point of view:
          → I'd like to predict what a user would like based on ratings or purchases from similar users.

  • Origin of the term:
    • Email system add-on called Tapestry devised by Goldberg et al [Gold1992] in 1992.
    • Users rate email content or record actions "e.g., replied".
    • Users filter email based on others' ratings or actions
            → e.g., "Include all emails that Alice replied to".

  • The current use is more technical:
    • Users rate some items.
    • Goal: predict a user's rating of some item they have not rated.
    • This approach was developed by the pioneering GroupLens project on movie ratings in 1994 [Resn1994].

  • Define ratings matrix Aij as follows
          Aij = user i's rating of item j.

  • Example with 5 users, 8 items:
    • Consider the following ratings matrix:
      
                        1   2   3   4   5   6   7   8
               Alice  |     1       5   5       3     |
               Bob    | 4       2           1       2 |
               Chen   |     1                         |
               Dave   |     2       4           3     |
               Ella   |     5       1   1           5 |
               
    • Alice and Bob have no overlap.
    • Alice and Dave have similar ratings.
            → Can predict Dave's rating of item 5 as "high".
    • Alice and Ella rate many items in common, but differently.
    • Chen offers very little data.
            → Hard to find a similar user.

  • The Netflix Challenge:
    • Started in October 2006.
    • Ratings from 480,189 users on 17,770 movies.
    • Total of 100,480,507 ratings.
    • Test test of about 1 million missing ratings.
    • Goal: predict missing ratings.
    • Challenge: beat Netflix's own algorithm by 10%.
    • Won by a combination of teams in 2009.
 

Approaches to CF:

  • Data-driven approach:
    • Use the data to compute similarities between users or items.
    • Generate a prediction based on the data from similar users/items.
    • Also called the memory-based approach.

  • Model-driven approach:
    • Use the data to build a statistical model.
            → And estimate that model's parameters.
    • Use model for prediction.

  • Hybrid approaches: these combine the above two.

  • Henceforth, we'll assume the following version of the CF problem:
    • We are given the user-item ratings matrix A.
    • The goal: predict missing entry Auv.
            → User u's rating of item v.
 

The data-driven approach:

  • This approach was pioneered by the GroupLens project [Resn1994].

  • Since then it has been refined and modified by several others.

  • Two kinds:
    • User-similarity: find similar users to user u who have rated v and use their ratings.
    • Item-similarity: find similar items to item v, and use the ratings of these items.

  • Let's first focus on the user-similarity approach.
    
    Algorithm: userSimilarity (A, u, v)
    Input: ratings matrix A, and desired missing entry A[u,v]
         // Compute distances to other users.
    1.   for each user i
    2.      s[i] = similarity (i, u)
    3.   endfor
    
         // Find a set of "close" or similar users.
    4.   similar-user-set S = φ
    5.   for each user i that has rated item j
    6.       if similarEnough (i, u)
    7.           add i to S
    8.       endif
    9.   endfor
    
         // Use the ratings of these users to predict user u's rating v.
    10.  r = combineRatings (S, v)
    11.  return r
         

  • The details of similarity() similarEnough() and combineRatings() differentiate various algorithms in this category.
          → We will only look at a few ideas - see the references for more details.

  • First, observe that rows of A are user-vectors:
    • For example, consider this matrix:
      
                        1   2   3   4   5   6   7   8
               Alice  |     1       5   5       3     |
               Bob    | 4       2           1       2 |
               Chen   |     1                         |
               Dave   |     2       4           3     |
               Ella   |     5       1   1           5 |
               
    • The vector for user Bob is: (4, 0, 2, 0, 0, 1, 0, 2) (assuming 0 is not a rating).

    Exercise: What is the cosine of the angle between the "Alice" and "Dave" vectors? What is the cosine of the angle between the "Alice" and "Chen" vectors?

  • Similarly, columns are item-vectors:
    • For example:
      
                        1   2   3   4   5   6   7   8
               Alice  |     1       5   5       3     |
               Bob    | 4       2           1       2 |
               Chen   |     1                         |
               Dave   |     2       4           3     |
               Ella   |     5       1   1           5 |
               

  • Computing similarity: several ways
    • Inverse Mean-square difference between the vectors for two rows.

    • Cosine of angle between vectors.

    • Pearson-correlation between the vectors.

  • Many ways of identifying the "close-enough" ones:
    • Keep the K closest ones (where K is an algorithm parameter).
    • Maintain a threshold δ and keep those users within this distance.

  • Combining ratings from close users on item v:
    • Average their ratings.

    • Take a weighted average, weighted by similarity

    • Offset weighted average by the mean.
            → Because some users tend to have high averages or low averages.

    • Note: κ is a normalizing factor.
    • More complex statistical techniques such as multiple-regression have been tried [Hill1995].

  • The item-similarity approach: [Sarw2001]
    • Recall: our goal is to estimate Auv.
    • In this approach: find a set of similar items I.
    • Use the ratings of users for the items in I.

  • How to find similar items?
    • Use the same types of vector-similarity measures as in comparing users.
            → Only this time, we use the columns of A.
    • Thus, items are similar if many users tend to rate them similarly.

  • How to combine ratings?
    • Again, using the same ideas as in user-similarity.
    • Find those items in I that have ratings for user u.
    • Combine those ratings as above.
 

Model-based approaches:

  • Model-based approaches try to uncover some underlying "structure" if such a structure exists or can be approximated.

  • For example: we "know" intuitively that users can be divided into "sci-fi movies", "romance movies" and the like.

  • If we can identify the user as one of these types, our prediction success might go up.

  • We'll next look at a few model-based approaches.
 

Simple clustering:

  • Create user categories.
          → These could be done by hand (e.g., "sci-fi", "romance" etc).

  • For each user, identify their category.

  • To compute Auv:
    • Identify u's category c.
    • Find users in c who have rated v.
    • Combine their ratings using some kind of averaging.

  • How do we classify users into categories?
    • Create "fake" user vectors, one for each category.
            → These serve as prototypes for the categories (ideal users).
    • Each such vector will have high ratings for the movies in that category, low for the others.
            → This means we need to "know" these categories.
    • Then, one can use vector-similarity with these category prototypes.
    • Example:
      
                        1   2   3   4   5   6   7   8
               Alice  |     1       5   5       3     |
               Bob    | 4       2           1       2 |
               Chen   |     1                         |
               Dave   |     2       4           3     |
               Ella   |     5       1   1           5 |
      
               Sci-fi | 0   5   0   5   0   0   0   0 |      // Movies 3 and 5 are sci-fi movies. Rest of the ratings are 0. 
               

    • How to do this without pre-defined categories:
      • Initially, we'll create K empty clusters.
      • Initially, assign each user to a random cluster.
      • Treat each user-vector as a point in n space.
      • Compute the average "point" (the cluster mean) in each cluster.
      • Now, for each user, identify closest mean and re-classify the user accordingly.
      • Repeat.

      This is called K-means clustering.

      
      Algorithm: K-means (A, u, v)
      Input: ratings matrix A, and desired missing entry A[u,v]
      1.   for each user i
              // Initially assign each user to a random cluster.
      2.      cluster[i] = random (1, K)
      3.   endfor
      
      4.   while not converged
              // Find the mean of each cluster and assign users to the cluster whose mean they are closest to.
      5.      Mk = computeMean (Ck)
      6.      for each user i
      7.         find closest cluster mean k
      8.         cluster[i] = k
      9.      endfor
      10.  endwhile
      
      11.  k = cluster for user u
      12.  S = users in Ck that have rated v
      13.  r = combineRatings (S, v)
      14.  return r
               
 

A probabilistic approach: the Naive-Bayes method

  • Let user u's vector of ratings be (u1,...,un).

  • We want to predict uv, one of the missing ones.

  • Suppose we treat this as the random variable Uv.

  • Then, in a 5-point rating system, such a random variable can take values 1,2, ... ,or 5.

  • We ask the question: what is
          Pr[Uv=r | u1,...,un].

  • That is, given the known ratings (u1,...,un). what is the probability that the unknown one is r?
          (Where r is either 1,2, ..., or 5.)

  • If we had these probabilities, how do we actually pick the rating?
          → The simplest way: pick the rating r whose probability Pr[Uv=r | u1,...,un]. is highest.

  • One way to do this:
    • Find other users who have rated both (u1,...,un). and uv.
    • Estimate Pr[Uv=r | u1,...,un]. from this data by simple averaging.
    • For this to be possible, the matrix A needs to be dense
            → The data has to be rich enough to find many such "high-overlap" users.
    • Unfortunately, this is not the case in practice (at least for movie data).
    • Some applications explicitly ask the user to rate 10-15 items so that all users have rated the same items.

  • So, let's examine a way around this problem: the Naive Bayes approach.

  • Using Bayes' rule
          Pr[Uv=r | u1,...,un]
          = Pr[u1,...,un | Uv=r] Pr[Uv=r] / Pr[u1,...,un]
    • Note that Pr[u1,...,un] is the simply the user-independent probability of observing the known ratings.
    • This is a "constant" for the system.
            → We will absorb it in a normalizing constant.
    • Thus, Pr[Uv=r | u1,...,un]   ∝   Pr[u1,...,un | Uv=r] Pr[Uv=r]

  • Next, we will assume independence between items and write
          Pr[u1,...,un | Uv=r]   =   Πi Pr[ui | Uv=r]

  • Each of these can be estimated because there's hope that we can find users who've rated both item v and item i.

  • Similarly, Pr[Uv=r] can be estimated from the column vector for item v.

  • Thus, we can estimate
          Pr[Uv=r | u1,...,un]   =   κ Pr[u1,...,un | Uv=r] Pr[Uv=r]   =   κ Pr[Uv=r] Πi Pr[ui | Uv=r]
    where κ is a normalizing constant.

  • In fact, we don't need to worry about the normalizing constant at all:
    • We are interested in picking that rating r for which Pr[Uv=r | u1,...,un] is largest.
    • This is the same as picking the rating for which
            Pr[Uv=r] Πi Pr[ui | Uv=r]
      is highest (without the normalizing constant).

  • Let's outline this approach with some pseudocode:
    
    Algorithm: naiveBayes (A, u, v)
    Input: ratings matrix A, and desired missing entry A[u,v]
          // Estimate Pr[Uv=r].
    1.    for each rating value r
    2.        num_v_r = # users that rated v as r
    3.        num_v = # users that rated v
    4.        RatingEstimate[r] = num_v_r / num_v
    5.    endfor
    
          // Estimate Pr[ui | Uv=r] for each possible i and r.
    6.    for each r
    7.        for each item j ≠ v
    8.            num_u_j_r = # users that rate j as uj and v as r
    9.            num_v_r = # users that rated v as r
    10.           conditionalRating[j] = num_u_j_r / num_v_r
    11.       endfor
    12.   endfor
    
          // Estimate Pr[Uv=r |  u1,...,un] using the product and identify the largest.
    13.   rmax = 0
    14.   for each r
    15.       product[r] = ratingEstimate[r]
    16.       for each item j ≠ v
    17.           product[r] = product[r] * conditionalRating[j]
    18.       endfor
    19.       if product[r] > rmax
    20.           rmax = product[r]
    21.       endif
    22.   endfor
    
    23.   return rmax
             

  • The Bayes approach has been used in book recommendation engines [Moon1999].
 

Machine learning:

  • The field of machine-learning has grown tremendously in the past 10-20 years.
          → Several sophisticated mc-learning algorithms have been developed.

  • There are many types of mc-learning problems.
          → The one most applicable to CF is classification.

  • The classification or supervised learning problem:
    • There are m so-called classes
            C1, ... ,Cm
    • We are given a d-dimensional input or query vector
            x = (x1, ... ,xd)
    • The objective: classify x into the "best" class.
    • To train a classifer, we are also give training data, a number of previously classified vectors:
            x1, y1    - training vector 1 and its known class y1
            x2, y2    - training vector 2 and its known class y2
            ...
            xk, yk    - training vector k and its known class yk

  • A machine learning algorithm:
    • Uses the training data to "train" a d-dimensional function F such that
            y = F(x) = F(x1, ..., xd).
    • Thus, F computes the class for an input vector
            x = (x1, ... ,xd)

  • How can machine-learning be applied to CF? Here are two simple ways:

  • First, for simplicity, let's re-order the matrix A's rows and columns so that the missing entry is at the bottom right.
    
                      1   2   3   4   5   6   7   8
             Alice  |     1       5   5       3   3 |
             Bob    | 4       2           1       2 |
             Chen   |     1                         |
             Ella   |     5       1   1           5 |
             Dave   |     2       4   2       3   ? |    // Desired missing entry is for last user and last item.
             

  • Then, one way to create a mc-learning problem is user-based:
    • The classes are the possible ratings 1, 2, ..., 5.
    • Treat the rows up to column d = n-1 as training vectors with the known classes in the last column.
      
                          TRAINING VECTORS             CLASS
               Alice  |     1       5   5       3  |     3 |
               Bob    | 4       2           1      |     2 |
               Ella   |     5       1   1          |     5 |
      
      		    QUERY VECTOR
               Dave   |     2       4   2       3  |     ? |    
               

  • The same thing could be item-based, using columns:
    • Treat the columns as vectors.
      
      	              TRAINING             QUERY
                            2     4   5     7      8
               Alice  |     1     5   5     3  |   3 |
               Bob    |                        |   2 |
               Chen   |     1                  |     |
               Ella   |     5     1   1        |   5 |
      
               Dave   |     2     4   2     3  |   ? |    <-  class (y) values
               

  • Two problems with this approach:
    • A zero rating is treated the same as a low rating.
    • The matrix is often too large (vectors too wide).

  • More sophisticated approaches:
    • Use matrix compression via factoring
            → Methods like SVD.
    • Specialized factoring or preprocessing.


References and further reading

 


[WP-1] Wikipedia entry on Collaborative filtering.
[WP-2] Wikipedia entry on the Stable Marriage problem.
[Agra1993] R.Agrawal, T.Imielinski and A.Swami. Mining Association Rules between Sets of Items in Large Databases. SIGMOD, 1993.
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