Applications. Nonnegative Matrices: Ranking
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1 Applications of Nonnegative Matrices: Ranking and Clustering Amy Langville Mathematics Department College of Charleston Hamilton Institute 8/7/2008
2 Collaborators Carl Meyer, N. C. State University David Gleich, Stanford University Michael Berry, University of Tennessee-Knoxville College of Charleston (current and former students) Luke Ingram Ibai Basabe Kathryn Pedings Neil Goodson Colin Stephenson Emmie Douglas
3 Nonnegative Matrices Ranking webpages Ranking sports teams Recommendation systems Meta-algorithms Rank and rating aggregation Cluster aggregation
4 Ranking webpages
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6 1998: enter Link Analysis uses hyperlink structure to focus the relevant set combine traditional IR score with popularity score Page and Brin 1998 Kleinberg
7 Web Information Retrieval IR before the Web = traditional IR IR on the Web = web IR
8 Web Information Retrieval IR before the Web = traditional IR IR on the Web = web IR How is the Web different from other document collections?
9 Web Information Retrieval IR before the Web = traditional IR IR on the Web = web IR How is the Web different from other document collections? It s huge. over 10 billion pages, average page size of 500KB 20 times size of Library of Congress print collection Deep Web X bigger than Surface Web
10 Web Information Retrieval IR before the Web = traditional IR IR on the Web = web IR How is the Web different from other document collections? It s huge. over 10 billion pages, average page size of 500KB 20 times size of Library of Congress print collection Deep Web X bigger than Surface Web It s dynamic. content changes: 40% of pages change in a week, 23% of.com change daily size changes: billions of pages added each year
11 Web Information Retrieval IR before the Web = traditional IR IR on the Web = web IR How is the Web different from other document collections? It s huge. over 10 billion pages, average page size of 500KB 20 times size of Library of Congress print collection Deep Web X bigger than Surface Web It s dynamic. content changes: 40% of pages change in a week, 23% of.com change daily size changes: billions of pages added each year It s self-organized. no standards, review process, formats errors, falsehoods, link rot, and spammers!
12 Web Information Retrieval IR before the Web = traditional IR IR on the Web = web IR How is the Web different from other document collections? It s huge. over 10 billion pages, average page size of 500KB 20 times size of Library of Congress print collection Deep Web X bigger than Surface Web It s dynamic. content changes: 40% of pages change in a week, 23% of.com change daily size changes: billions of pages added each year It s self-organized. no standards, review process, formats errors, falsehoods, link rot, and spammers! A Herculean Task!
13 Web Information Retrieval IR before the Web = traditional IR IR on the Web = web IR How is the Web different from other document collections? It s huge. over 10 billion pages, each about 500KB 20 times size of Library of Congress print collection Deep Web X bigger than Surface Web It s dynamic. content changes: 40% of pages change in a week, 23% of.com change daily size changes: billions of pages added each year It s self-organized. no standards, review process, formats errors, falsehoods, link rot, and spammers! Ah, but it s hyperlinked! Vannevar Bush s 1945 memex
14 Elements of a Web Search Engine WWW Crawler Module Page Repository User Indexing Module Results Queries query-independent Indexes Ranking Module Query Module Special-purpose indexes Content Index Structure Index
15 The Ranking Module (generates popularity scores) Measure the importance of each page
16 The Ranking Module (generates popularity scores) Measure the importance of each page The measure should be Independent of any query Primarily determined by the link structure of the Web Tempered by some content considerations
17 The Ranking Module (generates popularity scores) Measure the importance of each page The measure should be Independent of any query Primarily determined by the link structure of the Web Tempered by some content considerations Compute these measures off-line long before any queries are processed
18 The Ranking Module (generates popularity scores) Measure the importance of each page The measure should be Independent of any query Primarily determined by the link structure of the Web Tempered by some content considerations Compute these measures off-line long before any queries are processed Google s PageRank c technology distinguishes it from all competitors
19 The Ranking Module (generates popularity scores) Measure the importance of each page The measure should be Independent of any query Primarily determined by the link structure of the Web Tempered by some content considerations Compute these measures off-line long before any queries are processed Google s PageRank c technology distinguishes it from all competitors Google s PageRank = Google s $$$$$
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25 Google s PageRank (Lawrence Page & Sergey Brin 1998) The Google Goals Create a PageRank r(p ) that is not query dependent Off-line calculations No query time computation Let the Web vote with in-links But not by simple link counts One link to P from Yahoo! is important Many links to P from me is not Share The Vote Yahoo! casts many votes value of vote from Y ahoo! is diluted If Yahoo! votes for n pages Then P receives only r(y )/n credit from Y
26 Google s PageRank (Lawrence Page & Sergey Brin 1998) The Google Goals Create a PageRank r(p ) that is not query dependent Off-line calculations No query time computation Let the Web vote with in-links But not by simple link counts One link to P from Yahoo! is important Many links to P from me is not Share The Vote Yahoo! casts many votes value of vote from Y ahoo! is diluted If Yahoo! votes for n pages Then P receives only r(y )/n credit from Y
27 Google s PageRank (Lawrence Page & Sergey Brin 1998) The Google Goals Create a PageRank r(p ) that is not query dependent Off-line calculations No query time computation Let the Web vote with in-links But not by simple link counts One link to P from Yahoo! is important Many links to P from me is not Share The Vote Yahoo! casts many votes value of vote from Y ahoo! is diluted If Yahoo! votes for n pages Then P receives only r(y )/n credit from Y
28 Google s PageRank (Lawrence Page & Sergey Brin 1998) The Google Goals Create a PageRank r(p ) that is not query dependent Off-line calculations No query time computation Let the Web vote with in-links But not by simple link counts One link to P from Yahoo! is important Many links to P from me is not Share The Vote Yahoo! casts many votes value of vote from Y ahoo! is diluted If Yahoo! votes for n pages Then P receives only r(y )/n credit from Y
29 The Definition r(p )= r(p ) P P B P PageRank B P = {all pages pointing to P } P = number of out links from P
30 The Definition r(p )= r(p ) P P B P PageRank B P = {all pages pointing to P } P = number of out links from P Successive Refinement Start with r 0 (P i )=1/n for all pages P 1,P 2,...,P n
31 The Definition r(p )= r(p ) P P B P Successive Refinement Start with r 0 (P i )=1/n PageRank B P = {all pages pointing to P } P = number of out links from P for all pages P 1,P 2,...,P n Iteratively refine rankings for each page r 1 (P i )= r 0 (P ) P P B Pi
32 The Definition r(p )= r(p ) P P B P Successive Refinement Start with r 0 (P i )=1/n PageRank B P = {all pages pointing to P } P = number of out links from P for all pages P 1,P 2,...,P n Iteratively refine rankings for each page r 1 (P i )= r 0 (P ) P P B Pi r 2 (P i )= r 1 (P ) P P B Pi
33 The Definition r(p )= r(p ) P P B P Successive Refinement Start with r 0 (P i )=1/n PageRank B P = {all pages pointing to P } P = number of out links from P for all pages P 1,P 2,...,P n Iteratively refine rankings for each page r 1 (P i )= r 0 (P ) P P B Pi r 2 (P i )= r 1 (P ) P P B Pi... r j+1 (P i )= r j (P ) P P B Pi
34 In Matrix Notation After Step k π T k = [r k(p 1 ),r k (P 2 ),...,r k (P n )]
35 In Matrix Notation After Step k π T k = [r k(p 1 ),r k (P 2 ),...,r k (P n )] π T k+1 = πt k H where h ij = { 1/ Pi if i j 0 otherwise
36 In Matrix Notation After Step k π T k = [r k(p 1 ),r k (P 2 ),...,r k (P n )] π T k+1 = πt k H where h ij = { 1/ Pi if i j 0 otherwise PageRank vector = π T = lim k π T k = eigenvector for H Provided that the limit exists
37 Tiny Web 1 2 P 1 P 2 P 3 P 4 P 5 P H = P 1 P 2 P 3 P 4 P 5 P 6 4
38 H = Tiny Web P 1 P 2 P 3 P 4 P 5 P 6 P 1 0 1/2 1/ P 2 P 3 P 4 P 5 P 6 4
39 H = Tiny Web P 1 P 2 P 3 P 4 P 5 P 6 P 1 0 1/2 1/ P P 3 P 4 P 5 P 6 4
40 H = Tiny Web P 1 P 2 P 3 P 4 P 5 P 6 P 1 0 1/2 1/ P P 3 1/3 1/ /3 0 P 4 P 5 P 6 4
41 Tiny Web H = P 1 P 2 P 3 P 4 P 5 P 6 P 1 0 1/2 1/ P P 3 1/3 1/ /3 0 P /2 1/2 P 5 P 6 4
42 Tiny Web H = P 1 P 2 P 3 P 4 P 5 P 6 P 1 0 1/2 1/ P P 3 1/3 1/ /3 0 P /2 1/2 P /2 0 1/2 P 6 4
43 Tiny Web H = P 1 P 2 P 3 P 4 P 5 P 6 P 1 0 1/2 1/ P P 3 1/3 1/ /3 0 P /2 1/2 P /2 0 1/2 P
44 Tiny Web H = P 1 P 2 P 3 P 4 P 5 P 6 P 1 0 1/2 1/ P P 3 1/3 1/ /3 0 P /2 1/2 P /2 0 1/2 P A random walk on the Web Graph
45 Tiny Web H = P 1 P 2 P 3 P 4 P 5 P 6 P 1 0 1/2 1/ P P 3 1/3 1/ /3 0 P /2 1/2 P /2 0 1/2 P A random walk on the Web Graph PageRank = π i = amount of time spent at P i
46 Ranking with a Random Surfer Rank each page corresponding to a search term by number and quality of votes cast for that page. Hyperlink as vote 2 3 Markov chain 6 5 4
47 Ranking with a Random Surfer Rank each page corresponding to a search term by number and quality of votes cast for that page. Hyperlink as vote
48 Ranking with a Random Surfer Rank each page corresponding to a search term by number and quality of votes cast for that page. Hyperlink as vote
49 Ranking with a Random Surfer Rank each page corresponding to a search term by number and quality of votes cast for that page. Hyperlink as vote
50 Ranking with a Random Surfer Rank each page corresponding to a search term by number and quality of votes cast for that page. Hyperlink as vote
51 Ranking with a Random Surfer Rank each page corresponding to a search term by number and quality of votes cast for that page. Hyperlink as vote
52 Ranking with a Random Surfer Rank each page corresponding to a search term by number and quality of votes cast for that page. Hyperlink as vote
53 Ranking with a Random Surfer Rank each page corresponding to a search term by number and quality of votes cast for that page. Hyperlink as vote
54 Ranking with a Random Surfer Rank each page corresponding to a search term by number and quality of votes cast for that page. Hyperlink as vote
55 Ranking with a Random Surfer Rank each page corresponding to a search term by number and quality of votes cast for that page. Hyperlink as vote
56 Ranking with a Random Surfer Rank each page corresponding to a search term by number and quality of votes cast for that page. Hyperlink as vote 2 page 2 is a dangling node
57 Tiny Web H = P 1 P 2 P 3 P 4 P 5 P 6 P 1 0 1/2 1/ P P 3 1/3 1/ /3 0 P /2 1/2 P /2 0 1/2 P A random walk on the Web Graph PageRank = π i = amount of time spent at P i Dead end page (nothing to click on) a dangling node
58 Tiny Web H = P 1 P 2 P 3 P 4 P 5 P 6 P 1 0 1/2 1/ P P 3 1/3 1/ /3 0 P /2 1/2 P /2 0 1/2 P A random walk on the Web Graph PageRank = π i = amount of time spent at P i Dead end page (nothing to click on) a dangling node π T =(0, 1, 0, 0, 0, 0) = e-vector = Page P 2 is a rank sink
59 The Fix Allow Web Surfers To Make Random Jumps
60 Ranking with a Random Surfer Rank each page corresponding to a search term by number and quality of votes cast for that page. Hyperlink as vote 2 surfer teleports
61 The Fix Allow Web Surfers To Make Random Jumps ( 1 Replace zero rows with et n = n, 1 n,..., 1 ) n S = P 1 P 2 P 3 P 4 P 5 P 6 P 1 0 1/2 1/ P 2 1/6 1/6 1/6 1/6 1/6 1/6 P 3 1/3 1/ /3 0 P /2 1/2 P /2 0 1/2 P
62 The Fix Allow Web Surfers To Make Random Jumps ( 1 Replace zero rows with et n = n, 1 n,..., 1 ) n S = P 1 P 2 P 3 P 4 P 5 P 6 P 1 0 1/2 1/ P 2 1/6 1/6 1/6 1/6 1/6 1/6 P 3 1/3 1/ /3 0 P /2 1/2 P /2 0 1/2 P S = H + aet 6 is now row stochastic = ρ(s) = 1
63 The Fix Allow Web Surfers To Make Random Jumps ( 1 Replace zero rows with et n = n, 1 n,..., 1 ) n S = P 1 P 2 P 3 P 4 P 5 P 6 P 1 0 1/2 1/ P 2 1/6 1/6 1/6 1/6 1/6 1/6 P 3 1/3 1/ /3 0 P /2 1/2 P /2 0 1/2 P S = H + aet 6 is now row stochastic = ρ(s) = 1 Perron says π T 0 s.t. π T = π T S with i π i = 1
64 Nasty Problem The Web Is Not Strongly Connected
65 Nasty Problem The Web Is Not Strongly Connected S is reducible S = P 1 P 2 P 3 P 4 P 5 P 6 P 1 0 1/2 1/ P 2 1/6 1/6 1/6 1/6 1/6 1/6 P 3 1/3 1/ /3 0 P /2 1/2 P /2 0 1/2 P
66 Nasty Problem The Web Is Not Strongly Connected S is reducible S = P 1 P 2 P 3 P 4 P 5 P 6 P 1 0 1/2 1/ P 2 1/6 1/6 1/6 1/6 1/6 1/6 P 3 1/3 1/ /3 0 P /2 1/2 P /2 0 1/2 P Reducible = PageRank vector is not well defined Frobenius says S needs to be irreducible to ensure a unique π T > 0 s.t. π T = π T S with i π i = 1
67 Irreducibility Is Not Enough Could Get Trapped Into A Cycle (P i P j P i )
68 Irreducibility Is Not Enough Could Get Trapped Into A Cycle (P i P j P i ) The powers S k fail to converge
69 Irreducibility Is Not Enough Could Get Trapped Into A Cycle (P i P j P i ) The powers S k fail to converge π T k+1 = πt k S fails to convergence
70 Irreducibility Is Not Enough Could Get Trapped Into A Cycle (P i P j P i ) The powers S k fail to converge π T k+1 = πt k S fails to convergence Convergence Requirement Perron Frobenius requires S to be primitive
71 Irreducibility Is Not Enough Could Get Trapped Into A Cycle (P i P j P i ) The powers S k fail to converge π T k+1 = πt k S fails to convergence Convergence Requirement Perron Frobenius requires S to be primitive No eigenvalues other than λ = 1 on unit circle
72 Irreducibility Is Not Enough Could Get Trapped Into A Cycle (P i P j P i ) The powers S k fail to converge π T k+1 = πt k S fails to convergence Convergence Requirement Perron Frobenius requires S to be primitive No eigenvalues other than λ = 1 on unit circle Frobenius proved S is primitive S k > 0 for some k
73 The Google Fix Allow A Random Jump From Any Page G = αs +(1 α)e > 0, E = ee T /n, 0 <α<1
74 The Google Fix Allow A Random Jump From Any Page G = αs +(1 α)e > 0, E = ee T /n, 0 <α<1 G = αh + uv T > 0 u = αa +(1 α)e, v T = e T /n
75 The Google Fix Allow A Random Jump From Any Page G = αs +(1 α)e > 0, E = ee T /n, 0 <α<1 G = αh + uv T > 0 u = αa +(1 α)e, v T = e T /n PageRank vector π T = left-hand Perron vector of G
76 Ranking with a Random Surfer If a page is important, it gets lots of votes from other important pages, which means the random surfer visits it often. Simply count the number of times, or proportion of time, the surfer spends on each page to create ranking of webpages.
77 Ranking with a Random Surfer If a page is important, it gets lots of votes from other important pages, which means the random surfer visits it often. Simply count the number of times, or proportion of time, the surfer spends on each page to create ranking of webpages. Proportion of Time Page 1 =.04 Page 2 =.05 Page 3 =.04 Page 4 =.38 Page 5 =.20 Page 6 = Ranked List of Pages Page 4 Page 6 Page 5 Page 2 Page 1 Page 3
78 The Google Fix Allow A Random Jump From Any Page G = αs +(1 α)e > 0, E = ee T /n, 0 <α<1 G = αh + uv T > 0 u = αa +(1 α)e, v T = e T /n PageRank vector π T = left-hand Perron vector of G Some Happy Accidents x T G = αx T H + βv T Sparse computations with the original link structure
79 The Google Fix Allow A Random Jump From Any Page G = αs +(1 α)e > 0, E = ee T /n, 0 <α<1 G = αh + uv T > 0 u = αa +(1 α)e, v T = e T /n PageRank vector π T = left-hand Perron vector of G Some Happy Accidents x T G = αx T H + βv T Sparse computations with the original link structure λ 2 (G) =α Convergence rate controllable by Google engineers
80 The Google Fix Allow A Random Jump From Any Page G = αs +(1 α)e > 0, E = ee T /n, 0 <α<1 G = αh + uv T > 0 u = αa +(1 α)e, v T = e T /n PageRank vector π T = left-hand Perron vector of G Some Happy Accidents x T G = αx T H + βv T Sparse computations with the original link structure λ 2 (G) =α Convergence rate controllable by Google engineers v T can be any positive probability vector in G = αh + uv T
81 The Google Fix Allow A Random Jump From Any Page G = αs +(1 α)e > 0, E = ee T /n, 0 <α<1 G = αh + uv T > 0 u = αa +(1 α)e, v T = e T /n PageRank vector π T = left-hand Perron vector of G Some Happy Accidents x T G = αx T H + βv T Sparse computations with the original link structure λ 2 (G) =α Convergence rate controllable by Google engineers v T can be any positive probability vector in G = αh + uv T The choice of v T allows for personalization
82 Google matrix G > 0 Perron-Frobenius guarantees existence of π uniqueness of π convergence of algorithm rate of convergence: 2 (G) = α
83 Ranking Sports Teams
84 PageRank applied to Sports (joint work with Carl Meyer) webpages vote with hyperlinks losing teams vote with point differentials 45 Duke 45 Miami 20 VT UNC 2 UVA not as successful at ranking and predicting winners as mhits
85 mhits each team i gets both offensive rating o i and defensive rating d i mhits Thesis: A team is a good defensive team (i.e., deserves a high defensive rating d j ) when it holds its opponents (particularly strong offensive teams) to low scores. A team is a good offensive team (i.e., deserves a high offensive rating o i ) when it scores many points against its opponents (particularly opponents with high defensive ratings). Miami 17 UNC Duke VT UVA P= Duke Miami UNC UVA VT Duke Miami UNC UVA VT Graph Point Matrix P 0
86 Summation Notation mhits Equations d j = X i I j p ij 1 o i and o i = X j L i p ij 1 d j Matrix Notation: iterative procedure d (k) = P T 1 o (k) and o (k) = P 1 d (k 1) related to the Sinkhorn-Knopp algorithm for matrix balancing (uses successive row and column scaling to transform P 0 into doubly stochastic matrix S) P. Knight uses Sinkhorn-Knopp algorithm to rank webpages
87 mhits Results: tiny NCAA (data from Luke Ingram) Duke Miami 38 VT UNC 7 UVA 5
88 Weighted mhits Weighted Point Matrix P 0 p ij = w ij p ij (w ij = weight of matchup between teams i and j) possible weightings w ij w Linear w Logarithmic t 0 tf t t 0 tf t w Exponential w Step Function t 0 tf t t 0 t s tf t
89 mhits Results: full NCAA (image from Neil Goodson and Colin Stephenson)
90 mhits Results: full NCAA weightings can easily be applied to all ranking methods interesting possibilities for weighted webpage ranking
91 mhits Point Matrix P 0 Perron-Frobenius guarantees (for irreducible P with total support) existence of o and d uniqueness of o and d convergence of mhits algorithm rate of convergence of mhits algorithm σ2 2 (S), where S = D (1/o) P D (1/d)
92 Recommendation Systems
93 Recommendation Systems A 0 purchase history matrix A = Item Item Item m User 1 User 2... User n Find similar users Find similar items Cluster users into classes EX: use NMF so that A m n W m k H k n
94 NMF Algorithm: Lee and Seung 2000 Mean Squared Error objective function min ka WHk 2 F s.t. W, H 0 W = abs(randn(m,k)); H = abs(randn(k,n)); for i = 1 : maxiter H = H.* (W T A)./ (W T WH ); W = W.* (AH T )./ (WHH T ); end Many parameters affect performance (k, obj. function, sparsity constraints, algorithm, etc.). NMF is not unique! (proof of convergence to fixed point based on E-M convergence proof)
95 Interpretation with NMF columns of W are the underlying basis vectors, i.e., each of the n columns of A m n can be built from k columns of W m k. columns of H give the weights associated with each basis vector.... A 2 = A 1 WH 1 = item 1 5 item 2 0 item 3 0 item 4 11 item 5 1 w 1. WH 1 = h w 2. h w k. h k W, H 0 immediate interpretation (additive parts-based rep.)
96 user rating matrix A = Netflix movies,.5 million users movie movie movie m User 1 User 2... User n Find similar users Find similar movies Cluster users and movies into classes Rank users Rank movies
97 (vismatrix tool - David Gleich) Clustering Netflix (movie-movie matrix; x 17770)
98 user rating matrix A = Netflix movies,.5 million users movie movie movie m User 1 User 2... User n Find similar users Find similar movies Cluster users and movies into classes Rank users Rank movies
99 mhits on Netflix each movie i gets a rating m i and each user gets a rating u j mhits Thesis: A movie is a good (i.e., deserves a high rating m i ) if it gets high ratings from good (i.e., discriminating) users. A user is good (i.e., deserves a high rating u j ) when his or her ratings match the true rating of a movie. mhits Netflix Algorithm u = e; for i = 1 : maxiter m = A u; m = 5(m min(m)) max(m) min(m) ; u = 1 ((R (R>0). (em T )). 2 )e ; end
100 Netflix mhits results subset: 1500 super users 1 st Raiders of the Lost Ark 2 nd Silence of the Lambs 3 rd The Sixth Sense 4 th Shawshank Redemption 5 th LOR: Fellowship of the Ring 6 th The Matrix 7 th LOR: The Two Towers 8 th Pulp Fiction 9 th LOR: The Return of the King 10 th Forrest Gump 11 th The Usual Suspects 12 th American Beauty (rate 1000 movies) 13 th Pirates of the Carribbean: Black Pearl 14 th The Godfather 15 th Braveheart
101 The Enron Dataset (data from Mike Berry) PRIVATE collection of 150 Enron employees during ,000 terms and 65,000 messages Term-by-Message Matrix f astow1 f astow2 skilling subpoena dynegy
102 Text Mining Applications Data compression Find similar terms Find similar documents Cluster documents Cluster terms Topic detection and tracking
103 Unclustered Enron A
104 NMF-clustered Enron A
105 Clustering the Enron Dataset (image from Mike Berry)
106 Tracking Enron clusters over time (image from Mike Berry)
107 Meta-Algorithms
108 Rank Aggregation
109 Rank Aggregation average rank Borda count simulated data graph theory
110 Average Rank
111 Borda Count for each ranked list, each item receives a score equal to the number of items it outranks.
112 Simulated Data 3 ranked lists mhits VT beats Miami by 1 point, UVA by 2 points,... Miami beats UVA by 1 point, UNC by 2 points,... UVA beats UNC by 1 point, Duke by 2 points UNC beats Duke by 1 point repeat for each ranked list generates game scores for teams Simulated Game Data
113 Simulated Data
114 Graph Theory more voting ranked lists are used to form weighted graph possible weights * w ij = # of ranked lists having i below j * w ij = sum of rank differences of lists having i below j run algorithm (e.g., Markov, PageRank, HITS) to determine most important nodes
115 Aggregation Rating Aggregation
116 Rating Aggregation rating vectors form rating differential matrix R for each rating vector
117 Rating Aggregation rating differential matrices R 0 differing scales normalize
118 Rating Aggregation rating differential matrices
119 Rating Aggregation rating differential matrices combine into one matrix average
120 Rating Aggregation rating differential matrices combine into one matrix average
121 Rating Aggregation average rating differential matrix R average 0 run ranking method * Markov method on R T average * row sums of R average / col sums of R average * Perron vector of R average
122 Rating Aggregation average rating differential matrix R average 0 run ranking method * Markov method on R T average * row sums of R average / col sums of R average * Perron vector of R average
123 Cluster Aggregation many clustering algorithms = many clustering results Can we combine many results to make one super result? Cluster Aggregation Algorithm 1. Create aggregation matrix F 0 f ij = # of methods having items i and j in the same cluster 2. Run favorite clustering method on F
124 Cluster Aggregation Example Method 1 Method 2 Method 3 item cluster assignment item cluster assignment item cluster assignment Cluster Aggregated Graph Cluster Aggregated Results Fiedler using just one eigenvector Fiedler using two eigenvectors
125 Conclusions Applied problems often have nonnegative data. This nonnegativity is exploitable structure. proofs for convergence and rates of convergence proofs for existence and uniqueness Nonnegativity is easily interpretable. Often it pays to maintain nonnegativity throughout the modeling process. nonnegative matrix factorizations nonnegative rating vectors
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