34: The Zombie Day

1
3/4 The Zombie Day

• Feedback thingie..
• Last bits of clustering
• Review of any questions etc
• Assuming I actually remember..
• Filtering

2
Filtering and Recommender SystemsContent-based
and Collaborative
Some of the slides based On Mooneys Slides
3
Personalization

• Recommenders are instances of personalization
  software.
• Personalization concerns adapting to the
  individual needs, interests, and preferences of
  each user.
• Includes
• Recommending
• Filtering
• Predicting (e.g. form or calendar appt.
  completion)
• From a business perspective, it is viewed as part
  of Customer Relationship Management (CRM).

4
Feedback Prediction/Recommendation

• Traditional IR has a single userprobably working
  in single-shot modes
• Relevance feedback
• WEB search engines have
• Working continually
• User profiling
• Profile is a model of the user
• (and also Relevance feedback)
• Many users
• Collaborative filtering
• Propagate user preferences to other users

You know this one
5
Recommender Systems in Use

• Systems for recommending items (e.g. books,
  movies, CDs, web pages, newsgroup messages) to
  users based on examples of their preferences.
• Many on-line stores provide recommendations (e.g.
  Amazon, CDNow).
• Recommenders have been shown to substantially
  increase sales at on-line stores.

6
Feedback Detection
Non-Intrusive
Intrusive

• Click certain pages in certain order while ignore
  most pages.
• Read some clicked pages longer than some other
  clicked pages.
• Save/print certain clicked pages.
• Follow some links in clicked pages to reach more
  pages.
• Buy items/Put them in wish-lists/Shopping Carts

• Explicitly ask users to rate items/pages

7
3/11

• -Midterm returned
• --Two talks of interest tomorrow
• --Louiqa Raschid_at_10am
• --Zaiqing Nie_at_2pm

8
Midterm (In-class)

• 494 section
• Max 59.5Min 12Mean 32.17Stdev 11.98

• 598section
• Max 61.5Min 30Mean 47.4Stdev 10

• Overall class
• Max 61.5Min 12Mean 38.15Stdev 13.4

Pickup your exam at the end of the class
9
Midterm discussion
10
Content-based vs. Collaborative Recommendation
11
Collaborative Filtering
Correlation analysis Here is similar to
the Association clusters Analysis!
12
Collaborative Filtering Method

• Weight all users with respect to similarity with
  the active user.
• Select a subset of the users (neighbors) to use
  as predictors.
• Normalize ratings and compute a prediction from a
  weighted combination of the selected neighbors
  ratings.
• Present items with highest predicted ratings as
  recommendations.

13
Neighbor Selection

• For a given active user, a, select correlated
  users to serve as source of predictions.
• Standard approach is to use the most similar n
  users, u, based on similarity weights, wa,u
• Alternate approach is to include all users whose
  similarity weight is above a given threshold.

14
Rating Prediction

• Predict a rating, pa,i, for each item i, for
  active user, a, by using the n selected neighbor
  users,
• u ? 1,2,n.
• To account for users different ratings levels,
  base predictions on differences from a users
  average rating.
• Weight users ratings contribution by their
  similarity to the active user.

ri,j is user is rating for item j
15
Similarity Weighting

• Typically use Pearson correlation coefficient
  between ratings for active user, a, and another
  user, u.

ra and ru are the ratings vectors for the m
items rated by both a and u ri,j is
user is rating for item j
16
Covariance and Standard Deviation

• Covariance
• Standard Deviation

17
Significance Weighting

• Important not to trust correlations based on very
  few co-rated items.
• Include significance weights, sa,u, based on
  number of co-rated items, m.

18
Problems with Collaborative Filtering

• Cold Start There needs to be enough other users
  already in the system to find a match.
• Sparsity If there are many items to be
  recommended, even if there are many users, the
  user/ratings matrix is sparse, and it is 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 tastes.
• Tends to recommend popular items.
• WHAT DO YOU MEAN YOU DONT CARE FOR BRITNEY
  SPEARS YOU DUNDERHEAD?

19
Content-Based Recommending

• Recommendations are based on information on the
  content of items rather than on other users
  opinions.
• Uses machine learning algorithms to induce a
  profile of the users preferences from examples
  based on a featural description of content.
• Lots of systems

20
Advantages of Content-Based Approach

• No need for data on other users.
• No cold-start or sparsity problems.
• Able to recommend to users with unique tastes.
• Able to recommend new and unpopular items
• No first-rater problem.
• Can provide explanations of recommended items by
  listing content-features that caused an item to
  be recommended.
• Well-known technology The entire field of
  Classification Learning is at (y)our disposal!

21
Disadvantages of Content-Based Method

• Requires content that can be encoded as
  meaningful features.
• Users tastes must be represented as a learnable
  function of these content features.
• Unable to exploit quality judgments of other
  users.
• Unless these are somehow included in the content
  features.

22
Primer on Classification Learning
FAST

• (you can learn more about this in
• CSE 471 Intro to AI
• CSE 575 Datamining
• EEE 511 Neural Networks)

23
Many uses of Classification Learning in IR/Web
Search

• Learn user profiles
• Classify documents into categories based on their
  contents
• Useful in
• focused crawling
• Spam mail filtering
• Relevance reasoning..

24
A classification learning example Predicting when
Rusell will wait for a table
--similar to book preferences, predicting credit
card fraud, predicting when people are likely
to respond to junk mail
25
Uses different biases in predicting Russels
waiting habbits
Decision Trees --Examples are used to --Learn
topology --Order of questions
K-nearest neighbors
If patronsfull and dayFriday then wait
(0.3/0.7) If waitgt60 and Reservationno then
wait (0.4/0.9)
Association rules --Examples are used to
--Learn support and confidence of
association rules
SVMs
Neural Nets --Examples are used to --Learn
topology --Learn edge weights
Naïve bayes (bayesnet learning) --Examples are
used to --Learn topology --Learn CPTs
26
Mirror, Mirror, on the wall Which learning
bias is the best of all?
Well, there is no such thing, silly! --Each
bias makes it easier to learn some patterns and
harder (or impossible) to learn others -A
line-fitter can fit the best line to the data
very fast but wont know what to do if the data
doesnt fall on a line --A curve fitter can
fit lines as well as curves but takes longer
time to fit lines than a line fitter. --
Different types of bias classes (Decision trees,
NNs etc) provide different ways of naturally
carving up the space of all possible
hypotheses So a more reasonable question is --
What is the bias class that has a specialization
corresponding to the type of patterns that
underlie my data? -- In this bias class, what is
the most restrictive bias that still can capture
the true pattern in the data?
--Decision trees can capture all boolean
functions --but are faster at capturing
conjunctive boolean functions --Neural nets can
capture all boolean or real-valued functions
--but are faster at capturing linearly seperable
functions --Bayesian learning can capture all
probabilistic dependencies But are faster at
capturing single level dependencies (naïve bayes
classifier)
27
Fitting test cases vs. predicting future
cases The BIG TENSION.
2
1
3
Why not the 3rd?
28
Naïve Bayesian Classification

• Problem Classify a given example E into one of
  the classes among C1, C2 ,, Cn
• E has k attributes A1, A2 ,, Ak and each Ai can
  take d different values
• Bayes Classification Assign E to class Ci that
  maximizes P(Ci E)
• P(Ci E) P(E Ci) P(Ci) / P(E)
• P(Ci) and P(E) are a priori knowledge (or can be
  easily extracted from the set of data)
• Estimating P(ECi) is harder
• Requires P(A1v1 A2v2.AkvkCi)
• Assuming d values per attribute, we will need ndk
  probabilities
• Naïve Bayes Assumption Assume all attributes are
  independent P(E Ci) P P(Aivj Ci )
• The assumption is BOGUS, but it seems to WORK
  (and needs only ndk probabilities

29
NBC in terms of BAYES networks..
NBC assumption
More realistic assumption
30
Estimating the probabilities for NBC

• Given an example E described as A1v1
  A2v2.Akvk we want to compute the class of E
• Calculate P(Ci A1v1 A2v2.Akvk) for all
  classes Ci and say that the class of E is the
  one for which P(.) is maximum
• P(Ci A1v1 A2v2.Akvk)
• P P(vj Ci ) P(Ci) / P(A1v1
  A2v2.Akvk)
• Given a set of training N examples that have
  already been classified into n classes Ci
• Let (Ci) be the number of
  examples that are labeled as Ci
• Let (Ci, Aivi) be the number of
  examples labeled as Ci
• that have attribute Ai
  set to value vj
• P(Ci) (Ci)/N
• P(Aivj Ci) (Ci, Aivi) /
  (Ci)

31
Example
P(willwaityes) 6/12 .5 P(Patronsfullwillw
aityes) 2/60.333 P(Patronssomewillwaityes
) 4/60.666
Similarly we can show that P(Patronsfullwillw
aitno) 0.6666
P(willwaityesPatronsfull) P(patronsfullwill
waityes) P(willwaityes)

--------------------------------------------------
---------
P(Patronsfull)
k
.333.5 P(willwaitnoPatronsfull) k 0.666.5
32
Using M-estimates to improve probablity estimates

• The simple frequency based estimation of
  P(AivjCk) can be inaccurate, especially when
  the true value is close to zero, and the number
  of training examples is small (so the probability
  that your examples dont contain rare cases is
  quite high)
• Solution Use M-estimate
• P(Aivj Ci) (Ci, Aivi)
  mp / (Ci) m
• p is the prior probability of Ai taking the value
  vi
• If we dont have any background information,
  assume uniform probability (that is 1/d if Ai can
  take d values)
• m is a constantcalled equivalent sample size
• If we believe that our sample set is large
  enough, we can keep m small. Otherwise, keep it
  large.
• Essentially we are augmenting the (Ci) normal
  samples with m more virtual samples drawn
  according to the prior probability on how Ai
  takes values

Also, to avoid overflow errors do addition of
logarithms of probabilities (instead of
multiplication of probabilities)
33
Applying NBC for Text Classification

• Text classification is the task of classifying
  text documents to multiple classes
• Is this mail spam?
• Is this article from comp.ai or misc.piano?
• Is this article likely to be relevant to user X?
• Is this page likely to lead me to pages relevant
  to my topic? (as in topic-specific crawling)
• NBC has been applied a lot to text classification
  tasks.
• The big question How to represent text documents
  as feature vectors?
• Vector space variants (e.g. a binary version of
  the vector space rep)
• Used by Sahami et.al. in SPAM filtering
• A problem is that the vectors are likely to be as
  large as the size of the vocabulary
• Use feature selection techniques to select only
  a subset of words as features (see Sahami et al
  paper)
• Unigram model Mitchell paper
• Used by Joachims for newspaper article
  categorization
• Document as a vector of positions with values
  being the words

34
25th March

• Text Classification
• Spam mail filtering

35
Extensions to Naïve Bayes idea

• Vector of Bags model
• E.g. Books have several different fields that are
  all text
• Authors, description,
• A word appearing in one field is different from
  the same word appearing in another
• Want to keep each bag differentvector of m Bags

• Additional useful terms
• Odds Ratio
• P(relexample)/P(relexample)
• An example is positive if the odds ratio is gt 1
• Strengh of a keyword
• LogP(wrel)/P(wrel)
• We can summarize a users profile in terms of the
  words that have strength above some threshold.

36
Sahami et als Solution for SPAM detection

• use standard Term Vector Space model developed
  by Information Retrieval field (similar to
  AdEater)
• 1 e-mail message ? single fixed-width feature
  vector
• have 1 bit in this vector for each term that
  occurs in some message in E (plus a bunch of
  domain-specific featureseg, when message was
  sent)
• learning algorithm
• use standard Naive Bayes algorithm

37
Sahami et. Al. spam filtering

• The above framework is completely general. We
  just need to encode each e-mail as a fixed-width
  vector X ?X1, X2, X3, ..., XN? of features.
• So... What features are used in Sahamis system
• words
• suggestive phrases (free money, must be over
  21, ...)
• senders domain (.com, .edu, .gov, ...)
• peculiar punctuation (!!!Get Rich Quick!!!)
• did email contain an attachment?
• was message sent during evening or daytime?
• ?
• ?
• (Well see a similar list for AdEater and other
  learning systems)

generatedautomatically
handcrafted!
38
Feature Selection

• A problem -- too many features -- each vector x
  contains several thousand features.
• Most come from word features -- include a word
  if any e-mail contains it (eg, every x contains
  an opossum feature even though this word occurs
  in only one message).
• Slows down learning and predictoins
• May cause lower performance
• The Naïve Bayes Classifier makes a huge
  assumption -- the independence assumption.
• A good strategy is to have few features, to
  minimize the chance that the assumption is
  violated.
• Ideally, discard all features that violate the
  assumption. (But if we knew these features, we
  wouldnt need to make the naive independence
  assumption!)
• Feature selection a few thousand ? 500
  features

39
Feature-Selection approach

• Lots of ways to perform feature selection
• FEATURE SELECTION DIMENSIONALITY REDUCTION
• One simple strategy mutual information
• Suppose we have two random variables A and B.
• Mutual information MI(A,B) is a numeric measure
  of what we can conclude about A if we know B, and
  vice-versa.
• MI(A,B) Pr(AB) log(Pr(AB)/(Pr(A)Pr(B)))
• Example If A and B are independent, then we
  cant conclude anything MI(A, B) 0
• Note that MI can be calculated without needing
  conditional probabilities.

40
Mutual Information, continued

• Check our intuition independence -gt MI(A,B)0
  MI(A,B) Pr(AB) log(Pr(AB)/(Pr(A)Pr(B)))
  Pr(AB) log(Pr(A)Pr(B)/(Pr(A)Pr(B
  ))) Pr(AB) log 1
  0
• Fully correlated, it becomes the information
  content
• MI(A,A) - Pr(A)log(Pr(A))
• it depends on how uncertain the event is
  notice that the expression becomes maximum (1)
  when Pr(A).5 this makes sense since the most
  uncertain event is one whose probability is .5
  (if it is .3 then we know it is likely not to
  happen if it is .7 we know it is likely to
  happen).

41
MI and Feature Selection

• Back to feature selection Pick features Xi that
  have high mutual information with the junk/legit
  classification C.
• These are exactly the features that are good for
  prediction
• Pick 500 features Xi with highest value MI(Xi, C)
• NOTE NBCs estimate of probabilities is
  actually quite a bit wrong but they still got by
  with those..
• Also, note that this analysis looks at each
  feature in isolation and may thus miss highly
  predictive word groups whose individual words are
  quite non-predictive
• e.g. free and money may have low MI, but
  Free money may have higher MI.
• A way to handle this is to look at MI of not just
  words but subsets of words
• (in the worst case, you will need to compute 2n
  MIs ?)
• So instead, Sahami et. Al. add domain specific
  phrases separately..
• Note Theres no reason that the highest-MI
  features are the ones that least violate the
  independence assumption -- this is just a
  heuristic!

42
MI based feature selection vs. LSI

• Both MI and LSI are dimensionality reduction
  techniques
• MI is looking to reduce dimensions by looking at
  a subset of the original dimensions
• LSI looks instead at a linear combination of the
  subset of the original dimensions (Good Can
  automatically capture sets of dimensions that are
  more predictive. Bad the new features may not
  have any significance to the user)
• MI does feature selection w.r.t. a classification
  task (MI is being computed between a feature and
  a class)
• LSI does dimensionality reduction independent of
  the classes (just looks at data variance)

43
Experiments

• 1789 hand-tagged e-mail messages
• 1578 junk
• 211 legit
• Split into
• 1528 training messages (86)
• 251 testing messages (14)
• Similar to experiment described in AdEater
  lecture, except messages are not randomly split.
  This is unfortunate -- maybe performance is just
  a fluke.
• Training phase Compute PrXxCjunk, PrXx,
  and PCjunk from training messages
• Testing phase Compute PrCjunkXx for each
  training message x. Predict junk if
  PrCjunkXxgt0.999. Record mistake/correct
  answer in confusion matrix.

44
Precision/Recall Curves
better performance
Points from Table on Slide 14
45
Results
same configuration, just different training/test
messages
46
Real scenario

• Data in previous experiments was collected in a
  strange way. Real scenario tries to fix it.
• Three kinds of messages
• Read and keep
• Read and discard (eg, joke from a friend)
• Junk
• Real scenario models setting where messages
  arrive, and some are deleted because they are
  junk, others are deleted because they arent
  worth saving, and others are read and then saved.

Both of these shouldcount as legit -- but
read discard messageswere not collected
47
Summary

• Bayesian Classification
• Naïve Bayesian Classification
• Email features automatically generated lists of
  words hand-picked phrases domain-specific
  features
• Feature selection by Mutual Information heuristic
• Semi-controlled experiments
• Collect data in various ways compare 2/3
  categories
• Confusion Matrix
• Precision recall vs Accuracy
• Can trade precision for recall by varying
  classification threshold.

48
Current State of the Art in Spam Filtering

• SpamAssassin (http//www.spamassassin.org ) is
  pretty much the best spam filter out there (it is
  FREE!)
• Based on a variety of tests. Each test gives a
  numerical score (spam points) to the message
  (the more positive it is, the more spammy it is).
  When the cumulative scores is above a threshold,
  it puts the message in spam box. Tests used are
  at http//www.spamassassin.org/tests.html.
• Tests are 1 of three types
• Domain Specific Has a set of hand-written rules
  (sort of like the Sahami et. Al. domain specific
  features). If the rule matches then the message
  is given a score (ve or ve). If the cumulative
  score is more than a threshold, then the message
  is classified as SPAM..
• Bayesian Filter Uses NBC to train on messages
  that the user classified (requires that SA be
  integrated with a mail client ASU IMAP version
  does it)
• An interesting point is that it is hard to
  explain to the user why the bayesian filter
  found a message to be spam (while domain specific
  filter can say that specific phrases were found).
• Collaborative Filter E.g. Vipuls razor, etc. If
  this type of message has been reported as SPAM by
  other users (to a central spam server), then the
  message is given additional spam points.
• Messages are reported in terms of their
  signatures
• Simple checksum signatures dont quite work
  (since the Spammers put minor variations in the
  body)
• So, these techniques use fuzzy signatures, and
  similarity rather than equality of
  signatures. (see the connection with Crawling
  and Duplicate Detection).

49
A message caught by Spamassassin

• Message 346
• From aetbones_at_ccinet.ab.ca Thu Mar 25 165123
  2004
• From Geraldine Montgomery ltaetbones_at_ccinet.ab.cagt
  
• To randy.mullen_at_asu.edu
• Cc ranga_at_asu.edu, rangers_at_asu.edu, rao_at_asu.edu,
  raphael_at_asu.edu,
• rapture_at_asu.edu, rashmi_at_asu.edu
• Subject V1AGKRA 80 DISCOUNT !! sg g pz kf
• Date Fri, 26 Mar 2004 024921 0000 (GMT)
• X-Spam-Flag YES
• X-Spam-Checker-Version SpamAssassin 2.63
  (2004-01-11) on
• parichaalak.eas.asu.edu
• X-Spam-Level
  
• X-Spam-Status Yes, hits42.2 required5.0
  testsBIZ_TLD,DCC_CHECK,
• FORGED_MUA_OUTLOOK,FORGED_OUTLOOK_TAGS,HTM
  L_30_40,HTML_FONT_BIG,
• HTML_MESSAGE,HTML_MIME_NO_HTML_TAG,MIME_HT
  ML_NO_CHARSET,
• MIME_HTML_ONLY,MIME_HTML_ONLY_MULTI,MISSIN
  G_MIMEOLE,
• OBFUSCATING_COMMENT,RCVD_IN_BL_SPAMCOP_NET
  ,RCVD_IN_DSBL,RCVD_IN_NJABL,
• RCVD_IN_NJABL_PROXY,RCVD_IN_OPM,RCVD_IN_OP
  M_HTTP,
• RCVD_IN_OPM_HTTP_POST,RCVD_IN_SORBS,RCVD_I
  N_SORBS_HTTP,SORTED_RECIPS,

50
Example of SpamAssassin explanation
Domain specific

• X-Spam-Status Yes, hits42.2 required5.0
  testsBIZ_TLD,DCC_CHECK,
• FORGED_MUA_OUTLOOK,FORGED_OUTLOOK_TAGS,HTM
  L_30_40,HTML_FONT_BIG,
• HTML_MESSAGE,HTML_MIME_NO_HTML_TAG,MIME_HT
  ML_NO_CHARSET,
• MIME_HTML_ONLY,MIME_HTML_ONLY_MULTI,MISSIN
  G_MIMEOLE,
• OBFUSCATING_COMMENT,RCVD_IN_BL_SPAMCOP_NET
  ,RCVD_IN_DSBL,RCVD_IN_NJABL,
• RCVD_IN_NJABL_PROXY,RCVD_IN_OPM,RCVD_IN_OP
  M_HTTP,
• RCVD_IN_OPM_HTTP_POST,RCVD_IN_SORBS,RCVD_I
  N_SORBS_HTTP,SORTED_RECIPS,
• SUSPICIOUS_RECIPS,X_MSMAIL_PRIORITY_HIGH,X
  _PRIORITY_HIGH autolearnno
• version2.63

collaborative
In this case, autolearn is set to no so bayesian
filter is not active.
51
General comments on Spam

• Spam is a technical problem (we created it)
• It has the arms-race character to it
• We cant quite legislate against SPAM
• Most spam comes from outside national boundaries
• Need technical solutions
• To detect Spam (we mostly have a handle on it)
• To STOP spam generation (detecting spam after its
  gets sent still is taxing mail serversby some
  estimates more than 66 of the mail relayed by
  AOL/Yahoo mailservers is SPAM
• Brother Gates suggest monetary cost
• Make every mailer pay for the mail they send
• Not necessarily in stamps but perhaps by
  agreeing to give some CPU cycles to work on some
  problem (e.g. finding primes computing PI etc)
• The cost will be minuscule for normal users, but
  will multiply for spam mailers who send millions
  of mails.
• Other innovative ideas neededwe now have a
  conferences on Spam mail
• http//www.ceas.cc/

52
NBC with Unigram Model

• Assume that words from a fixed vocabulary V
  appear in the document D at different positions
  (assume D has L words)
• P(DC) is P(p1w1,p2w2pLwl C)
• Assume that words appearance probabilities are
  independent of each other
• P(DC) is P(p1w1C)P(p2w2C) P(pLwl C)
• Assume that word occurrence probability is
  INDEPENDENT of its position in the document
• P(p1w1C)P(p2w1C)P(pLw1C)
• Use m-estimates set p to 1/V and m to V (where V
  is the size of the vocabulary)
• P(wkCi) (wk,Ci) 1/w(Ci) V
• (wk,Ci) is the number of times wk appears in the
  documents classified into class Ci
• w(Ci) is the total number of words in all
  documents

Used to classify usenet articles from 20
different groups --achieved an accuracy of
89!! (random guessing will get you 5)
53
How Well (and WHY) DOES NBC WORK?

• Naïve bayes classifier is darned easy to
  implement
• Good learning speed, classification speed
• Modest space storage
• Supports incrementality
• Recommendations re-done as more attribute values
  of the new item become known.
• It seems to work very well in many scenarios
• Peter Norvig, the director of Machine Learning at
  GOOGLE said, when asked about what sort of
  technology they use Naïve bayes
• But WHY?
• Domingos/Pazzani 1996 showed that NBC has much
  wider ranges of applicability than previously
  thought (despite using the independence
  assumption)
• classification accuracy is different from
  probability estimate accuracy
• Notice that normal classification application
  application dont quite care about the actual
  probability only which probability is the
  highest
• Exception is Cost-based learningsuppose false
  positives and false negatives have different
  costs
• E.g. Sahami et al consider a message to be spam
  only if Spam class probability is gt.9 (so they
  are using incorrect NBC estimates here)

54
Combining Content and Collaboration

• Content-based and collaborative methods have
  complementary strengths and weaknesses.
• Combine methods to obtain the best of both.
• Various hybrid approaches
• Apply both methods and combine recommendations.
• Use collaborative data as content.
• Use content-based predictor as another
  collaborator.
• Use content-based predictor to complete
  collaborative data.

55
Movie Domain

• EachMovie Dataset Compaq Research Labs
• Contains user ratings for movies on a 05 scale.
• 72,916 users (avg. 39 ratings each).
• 1,628 movies.
• Sparse user-ratings matrix (2.6 full).
• Crawled Internet Movie Database (IMDb)
• Extracted content for titles in EachMovie.
• Basic movie information
• Title, Director, Cast, Genre, etc.
• Popular opinions
• User comments, Newspaper and Newsgroup reviews,
  etc.

56
Content-Boosted Collaborative Filtering
EachMovie
IMDb
57
Content-Boosted CF - I
58
Content-Boosted CF - II
User Ratings Matrix
Pseudo User Ratings Matrix
Content-Based Predictor

• Compute pseudo user ratings matrix
• Full matrix approximates actual full user
  ratings matrix
• Perform CF
• Using Pearson corr. between pseudo user-rating
  vectors

59
Conclusions

• Recommending and personalization are important
  approaches to combating information over-load.
• Machine Learning is an important part of systems
  for these tasks.
• Collaborative filtering has problems.
• Content-based methods address these problems (but
  have problems of their own).
• Integrating both is best.