http://cs246.stanford.edu It is always possible to decompose a real - - PowerPoint PPT Presentation
Announcements: Submit your project group TODAY (Ed Pinned Post) Project Proposal due this Thursday (no late periods) Upload homework on time (23:59pm)! CS246: Mining Massive Datasets Jure Leskovec, Stanford University
CS246: Mining Massive Datasets Jure Leskovec, Stanford University
Announcements:
U, , V: unique* U, V: column orthonormal
▪ UT U = I; VT V = I (I: identity matrix) ▪ (Columns are orthogonal unit vectors)
: diagonal
▪ Entries (singular values) are positive, and sorted in decreasing order (σ1 σ2 ... 0)
* Up to permutations for redundant singular values and orientation of singular vectors (details)
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 2
High dim. data
Locality sensitive hashing Clustering Dimension- ality reduction
Graph data
PageRank, SimRank Community Detection Spam Detection
Infinite data
Sampling data streams Filtering data streams Queries on streams
Machine learning
SVM Decision Trees Perceptron, kNN
Apps
Recommen- der systems Association Rules Duplicate document detection
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 3
Customer X
▪ Buys Metallica CD ▪ Buys Megadeth CD
Customer Y
▪ Does search on Metallica ▪ Recommender system suggests Megadeth from data collected about customer X
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 4
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 5
Items Search Recommendations Products, web sites, blogs, news items, …
Examples:
Shelf space is a scarce commodity for
▪ Also: TV networks, movie theaters,…
Web enables near-zero-cost dissemination
▪ From scarcity to abundance
More choice necessitates better filters:
▪ Recommendation engines ▪ Association rules: How Into Thin Air made Touching the Void a bestseller:
http://www.wired.com/wired/archive/12.10/tail.html
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 6
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 7
Source: Chris Anderson (2004)
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 8
Read http://www.wired.com/wired/archive/12.10/tail.html to learn more!
Editorial and hand curated
▪ List of favorites ▪ Lists of “essential” items
Simple aggregates
▪ Top 10, Most Popular, Recent Uploads
Tailored to individual users
▪ Amazon, Netflix, …
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 9
Today’s class
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 10
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 11
Avatar LOTR Matrix Pirates Alice Bob Carol David
(1) Gathering “known” ratings for matrix
▪ How to collect the data in the utility matrix
(2) Extrapolating unknown ratings from the
▪ Mainly interested in high unknown ratings
▪ We are not interested in knowing what you don’t like but what you like
(3) Evaluating extrapolation methods
▪ How to measure success/performance of recommendation methods
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 12
Explicit
▪ Ask people to rate items ▪ Doesn’t work well in practice – people don’t like being bothered ▪ Crowdsourcing: Pay people to label items
Implicit
▪ Learn ratings from user actions
▪ E.g., purchase implies high rating ▪ E.g., add to playlist, play in full, skip song…
▪ What about low ratings?
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 13
Key problem: Utility matrix U is sparse
▪ Most people have not rated most items ▪ Cold Start Problem:
▪ New items have no ratings ▪ New users have no history
Three approaches to recommender systems:
▪ 1) Content-based ▪ 2) Collaborative ▪ 3) Latent factor based
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 14
Main idea: Recommend items to customer x
Movie recommendations
▪ Recommend movies with same actor(s), director, genre, …
Websites, blogs, news
▪ Recommend other sites with “similar” content
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 16
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 17
likes
Item profiles
Red Circles Triangles
User profile
match recommend build
For each item, create an item profile Profile is a set (vector) of features
▪ Movies: author, title, actor, director,… ▪ Text: Set of “important” words in document
How to pick important features?
▪ Usual heuristic from text mining is TF-IDF (Term frequency * Inverse Doc Frequency)
▪ Term … Feature ▪ Document … Item
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 18
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 19
Note: we normalize TF to discount for “longer” documents
Large when term i appears often in doc j Large when term i appears in very few documents Added pink notes
User profile possibilities:
▪ Weighted average of rated item profiles ▪ Variation: weight by difference from average rating for item
Prediction heuristic: Cosine similarity of user
▪ Given user profile x and item profile i, estimate 𝑣 𝒚, 𝒋 = cos 𝒚, 𝒋 =
𝒚·𝒋 𝒚 ⋅ 𝒋
How do you quickly find items closest to 𝒚?
▪ Job for LSH!
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 20
+: No need for data on other users
▪ No cold-start or sparsity problems
+: Able to recommend to users with
+: Able to recommend new & unpopular items
▪ No first-rater problem
+: Able to provide explanations
▪ Can provide explanations of recommended items by listing content-features that caused an item to be recommended
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 21
–: Finding the appropriate features is hard
▪ E.g., images, movies, music
–: Recommendations for new users
▪ How to build a user profile?
–: Overspecialization
▪ Never recommends items outside user’s content profile ▪ People might have multiple interests ▪ ! Unable to exploit quality judgments of other users!
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 22
Consider user x Find set N of other
Estimate x’s ratings
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 24
x N
Let rx be the vector of user x’s ratings Jaccard similarity metric
▪ Problem: Ignores the value of the rating
Cosine similarity metric
▪ sim(x, y) = cos(rx, ry) =
𝑠𝑦⋅𝑠𝑧 ||𝑠𝑦||⋅||𝑠𝑧||
▪ Problem: Treats some missing ratings as “negative”
Better: Pearson correlation coefficient
▪ Sxy = items rated by both users x and y
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 25
rx = [*, _, _, *, ***] ry = [*, _, **, **, _]
rx, ry as sets: rx = {1, 4, 5} ry = {1, 3, 4} rx, ry as points: rx = {1, 0, 0, 1, 3} ry = {1, 0, 2, 2, 0}
rx, ry … avg. rating of x, y
Intuitively we want: sim(A, B) > sim(A, C) Jaccard similarity: 1/5 < 2/4 Cosine similarity: 0.380 > 0.322
▪ Considers missing ratings as “negative” ▪ Solution: subtract the (row) mean
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 26
sim A,B vs. A,C: 0.092 > -0.559
Notice cosine sim. is correlation when data is centered at 0
𝒕𝒋𝒏(𝒚, 𝒛) = σ𝒋 𝒔𝒚𝒋 ⋅ 𝒔𝒛𝒋 σ𝒋 𝒔𝒚𝒋
𝟑 ⋅
σ𝒋 𝒔𝒛𝒋
𝟑
Cosine sim:
Let rx be the vector of user x’s ratings Let N be the set of k users most similar to x
Prediction for item i of user x:
▪ 𝑠𝑦𝑗 =
1 𝑙 σ𝑧∈𝑂 𝑠𝑧𝑗
▪ Or even better:
Many other tricks possible…
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 27
Shorthand: 𝒕𝒚𝒛 = 𝒕𝒋𝒏 𝒚, 𝒛
So far: User-user collaborative filtering Another view: Item-item
▪ For item i, find other similar items ▪ Estimate rating for item i based
▪ Can use same similarity metrics and prediction functions as in user-user model
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 28
) ; ( ) ; ( x i N j ij x i N j xj ij xi
sij… similarity of items i and j rxj…rating of user x on item j N(i;x)… set items which were rated by x and similar to i
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 29
12 11 10 9 8 7 6 5 4 3 2 1 4 5 5 3 1 1 3 1 2 4 4 5 2 5 3 4 3 2 1 4 2 3 2 4 5 4 2 4 5 2 2 4 3 4 5 4 2 3 3 1 6 users movies
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 30
12 11 10 9 8 7 6 5 4 3 2 1 4 5 5 ? 3 1 1 3 1 2 4 4 5 2 5 3 4 3 2 1 4 2 3 2 4 5 4 2 4 5 2 2 4 3 4 5 4 2 3 3 1 6 users
movies
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 31
12 11 10 9 8 7 6 5 4 3 2 1 4 5 5 ? 3 1 1 3 1 2 4 4 5 2 5 3 4 3 2 1 4 2 3 2 4 5 4 2 4 5 2 2 4 3 4 5 4 2 3 3 1 6 users
Neighbor selection: Identify movies similar to movie 1, rated by user 5
movies 1.00
0.41
0.59
Here we use Pearson correlation as similarity: 1) Subtract mean rating mi from each movie i m1 = (1+3+5+5+4)/5 = 3.6 row 1: [-2.6, 0, -0.6, 0, 0, 1.4, 0, 0, 1.4, 0, 0.4, 0] 2) Compute dot products between rows
s1,m
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 32
12 11 10 9 8 7 6 5 4 3 2 1 4 5 5 ? 3 1 1 3 1 2 4 4 5 2 5 3 4 3 2 1 4 2 3 2 4 5 4 2 4 5 2 2 4 3 4 5 4 2 3 3 1 6 users
Compute similarity weights:
s1,3=0.41, s1,6=0.59 movies 1.00
0.41
0.59
s1,m
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 33
12 11 10 9 8 7 6 5 4 3 2 1 4 5 5
2.6
3 1 1 3 1 2 4 4 5 2 5 3 4 3 2 1 4 2 3 2 4 5 4 2 4 5 2 2 4 3 4 5 4 2 3 3 1 6 users
Predict by taking weighted average: r1.5 = (0.41*2 + 0.59*3) / (0.41+0.59) = 2.6
movies 𝒔𝒋𝒚 = σ𝒌∈𝑶(𝒋;𝒚)𝒕𝒋𝒌 ⋅ 𝒔𝒌𝒚 σ𝒕𝒋𝒌
Define similarity sij of items i and j Select k nearest neighbors N(i; x)
▪ Items most similar to i, that were rated by x
Estimate rating rxi as the weighted average:
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 34
baseline estimate for rxi
μ = overall mean movie rating
bx = rating deviation of user x = (avg. rating of user x) – μ
bi = rating deviation of movie i
=
) ; ( ) ; ( x i N j ij x i N j xj ij xi
s r s r Before:
) ; ( ) ; (
x i N j ij x i N j xj xj ij xi xi
𝒄𝒚𝒋 = 𝝂 + 𝒄𝒚 + 𝒄𝒋
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 35
Avatar LOTR Matrix Pirates Alice Bob Carol David
In practice, it has been observed that item-item
Why? Items are simpler, users have multiple tastes
+ Works for any kind of item
▪ No feature selection needed
- Cold Start:
▪ Need enough users in the system to find a match
- Sparsity:
▪ The user/ratings matrix is sparse ▪ Hard to find users that have rated the same items
- First rater:
▪ Cannot recommend an item that has not been previously rated ▪ New items, Esoteric items
- Popularity bias:
▪ Cannot recommend items to someone with unique taste ▪ Tends to recommend popular items
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 36
Implement two or more different
▪ Perhaps using a linear model
Add content-based methods to
▪ Item profiles for new item problem ▪ Demographics to deal with new user problem
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 37
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 38
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 39
1 3 4 3 5 5 4 5 5 3 3 2 2 2 5 2 1 1 3 3 1 movies users
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 40
1 3 4 3 5 5 4 5 5 3 3 2 ? ? ? 2 1 ? 3 ? 1 Test Data Set users movies
Compare predictions with known ratings
▪ Root-mean-square error (RMSE)
▪
1 𝑂σ𝑦𝑗 𝑠𝑦𝑗 − 𝑠 𝑦𝑗 ∗ 2 where 𝒔𝒚𝒋 is predicted, 𝒔𝒚𝒋 ∗ is the true rating of x on i
▪ N is the number of points we are making comparisons on
▪ Rank Correlation:
▪ Spearman’s correlation between system’s and user’s complete rankings
▪ Precision at top 10 (or k):
▪ % of those in top 10 (or k)
Another approach: 0/1 model
▪ Coverage:
▪ Number of items/users for which the system can make predictions
▪ Precision:
▪ Accuracy of predictions
▪ Receiver operating characteristic (ROC)
▪ Tradeoff curve between false positives and false negatives
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 41
Idea: ignore lowly-ranked items
Added green note & rearranged order of bullets
Narrow focus on accuracy sometimes
▪ Prediction Diversity ▪ Prediction Context ▪ Order of predictions
In practice, we care only to predict high
▪ RMSE might penalize a method that does well for high ratings and badly for others
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 42
Expensive step is finding k most similar
Too expensive to do at runtime
▪ Could pre-compute
Pre-computation takes time O(k ·|X|)
▪ X … set of customers
We already know how to do this!
▪ Near-neighbor search in high dimensions (LSH) ▪ Clustering ▪ Dimensionality reduction
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 43
Leverage all the data
▪ Don’t try to reduce data size in an effort to make fancy algorithms work ▪ Simple methods on large data do best
Add more data
▪ e.g., add IMDB data on genres
More data beats better algorithms
http://anand.typepad.com/datawocky/2008/03/more-data-usual.html
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 44
Training data
▪ 100 million ratings, 480,000 users, 17,770 movies
▪ Lots of ratings – still 99% sparsity!
▪ 6 years of data: 2000-2005
Test data (private)
▪ Last few ratings of each user (2.8 million) ▪ Evaluation criterion: root mean squared error (RMSE) ▪ Netflix Cinematch RMSE (production): 0.9514
Competition
▪ 2700+ teams ▪ $1 million prize for 10% improvement on Cinematch
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 46
Next topic: Recommendations via
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 47
Overview of Coffee Varieties
FR TE S6 S5 L5 S3 S2 S1 R8 R6 R5 R4 R3 R2 L4 C7 S7 F9 F8 F6 F5 F4 F3 F2F1 F0 I2 C6 I1 C4 C3 C2 C1 B2 B1 S4 Complexity of Flavor Exoticness / Price Flavored Exotic Popular Roasts and Blends a1
The bubbles above represent products sized by sales volume. Products close to each other are recommended to each other.
Geared towards females Geared towards males serious Less serious The Princess Diaries The Lion King Braveheart Independence Day Amadeus The Color Purple Ocean’s 11 Sense and Sensibility
Gus Dave
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 48
[slide from winning BellkorTeam] Lethal Weapon Dumb and Dumber
Koren, Bell, Volinksy, IEEE Computer , 2009
4/20/2020 Tim Althoff, UW CS547: Machine Learning for Big Data, http://www.cs.washington.edu/cse547 49