Project Presentations

1
Project Presentations

• Thursday next week, each student will make a
  4-minute presentation on their project in class
  (with 1 or 2 minutes for questions)
• Email me your Powerpoint or PDF slides, with your
  name (e.g., joesmith.ppt), before 10am next
  Thursday
• Suggested content
• Definition of the task/goal
• Description of data sets
• Description of algorithms
• Experimental results and conclusions
• Be visual where possible! (i.e., use figures,
  graphs, etc)
• Final project report will be due by 12 noon
  Tuesday of finals week more details to come
  later

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ICS 278 Data MiningLecture 18 Analysis of Web
User Data

• Padhraic Smyth
• Department of Information and Computer Science
• University of California, Irvine

3
Outline

• Basic concepts in Web mining
• Analyzing user navigation or clickstream data
• Predictive modeling of Web navigation behavior
• Markov modeling methods
• Analyzing search engine data
• Ecommerce aspects of Web log mining
• Automated recommender systems

4
Further Reading

• Modeling the Internet and the Web, P. Baldi, P.
  Frasconi, P. Smyth, Wiley, 2003.
• ACM Transactions on Internet Technology (ACM
  TOIT) can be accessed via ACM Digital Library
  (available from UCI IP addresses).
• Annual WebKDD workshops at the ACM SIGKDD
  conferences.
• Papers on Web page prediction
• Selective Markov models for predicting Web page
  accesses, M. Deshpande, G. Karypis, ACM
  Transactions on Internet Technology, May 2004.
• Model-based clustering and visualization of
  navigation patterns on a Web site, Cadez et al,
  Journal of Data Mining and Knowledge Discovery,
  2003.

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Introduction to Web Mining

• Useful to study human digital behavior, e.g.
  search engine data can be used for
• Exploration e.g. of queries per session?
• Modeling e.g. any time of day dependence?
• Prediction e.g. which pages are relevant?
• Applications
• Understand social implications of Web usage
• Design of better tools for information access
• E-commerce applications

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Advertising Applications

• Revenue of many internet companies is driven by
  advertising
• Key problem
• Given user data
• Pages browsed
• Keywords used in search
• Demographics
• Determine the most relevant ads (in real-time)
• Currently about 50 of keyword searches can not
  be matched effectively to any ads
• (other aspects include bidding/pricing of ads)
• Another major problem click fraud
• Algorithms that can automatically detect when
  online advertisements are being manipulated (this
  is a major problem for Internet advertising)
• Understanding the user is key to these types of
  applications

7
Data Sources for Web Mining

• Web content
• Text and HTML content on Web pages, e.g.,
  categorization of content
• Web connectivity
• Hyperlink/directed-graph structure of the Web
• e.g., using PageRank to infer importance of Web
  pages
• e.g., using links to improve accuracy in
  classification of Web pages
• Web user data
• Data on how users interact with the Web
• Navigation data, aka clickstream data
• Search query data (keywords for users)
• Online transaction data (e.g., purchases at an
  ecommerce store)
• Volume of data?
• Large portals (e.g., Yahoo!, MSN) report 100s of
  millions of users per month

8
Flowchart of a typical Web Mining process (From
Cooley, ACM TOIT, 2003)
9
How our Web navigation is recorded

• Web logs
• Record activity between client browser and a
  specific Web server
• Easily available
• Can be augmented with cookies (provide notion of
  state)
• Search engine records
• Text in queries, which pages were viewed, which
  snippets were clicked on, etc
• Client-side browsing records
• Automatically recorded by client-side software
• Harder to obtain, but much more accurate than
  server-side logs
• Other sources
• Web site registration, purchases, email, etc
• ISP recording of Web browsing

10
Web Server Log Files

• Server Transfer Log
• transactions between a browser and server are
  logged
• IP address, the time of the request
• Method of the request (GET, HEAD, POST)
• Status code, a response from the server
• Size in byte of the transaction
• Referrer Log
• where the request originated
• Agent Log
• browser software making the request (spider)
• Error Log
• request resulted in errors (404)

11
W3C Extended Log File Format
12
Example of Web Log entries

• Apache web log
• 205.188.209.10 - - 29/Mar/2002035806 -0800
  "GET /sophal/whole5.gif HTTP/1.0" 200 9609
  "http//www.csua.berkeley.edu/sophal/whole.html"
  "Mozilla/4.0 (compatible MSIE 5.0 AOL 6.0
  Windows 98 DigExt)"
• 216.35.116.26 - - 29/Mar/2002035940 -0800
  "GET /alexlam/resume.html HTTP/1.0" 200 2674 "-"
  "Mozilla/5.0 (Slurp/cat slurp_at_inktomi.com
  http//www.inktomi.com/slurp.html)
• 202.155.20.142 - - 29/Mar/2002030014 -0800
  "GET /tahir/indextop.html HTTP/1.1" 200 3510
  "http//www.csua.berkeley.edu/tahir/"
  "Mozilla/4.0 (compatible MSIE 6.0 Windows NT
  5.1)
• 202.155.20.142 - - 29/Mar/2002030014 -0800
  "GET /tahir/animate.js HTTP/1.1" 200 14261
  "http//www.csua.berkeley.edu/tahir/indextop.html
  " "Mozilla/4.0 (compatible MSIE 6.0 Windows NT
  5.1)

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Routine Server Log Analysis

• Typical statistics/histograms that are computed
• Most and least visited web pages
• Entry and exit pages
• Referrals from other sites or search engines
• What are the searched keywords
• How many clicks/page views a page received
• Error reports, like broken links
• Many software products that produce standard
  reports of this type of data
• Very useful for Web site managers
• But does not provide deep insights
• e.g., are there clusters/groups of users that use
  the site in different ways?

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Visualization of Web Log Data over Time
15
Descriptive Summary Statistics

• Histograms, scatter plots, time-series plots
• Very important!
• Helps to understand the big picture
• Provides marginal context for any
  model-building
• models aggregate behavior, not individuals
• Challenging for Web log data
• Examples
• Session lengths (e.g., power laws)
• Click rates as a function of time, content

16
L number of page requests in a single
session from visitors to www.ics.uci.edu over 1
week in November 2002 (robots removed)
17
Best fit of simple power law model Log P(L) -a
Log L b or P(L) b L-a
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Web data measurement issues

• Important to understand how data is collected
• Web data is collected automatically via software
  logging tools
• Advantage
• No manual supervision required
• Disadvantage
• Data can be skewed (e.g. due to the presence of
  robot traffic)
• Important to identify robots (also known as
  crawlers, spiders)

20
A time-series plot of ICS Website data
Number of page requests per hour as a function of
time from page requests in the www.ics.uci.edu
Web server logs during the first week of April
2002.
21
Example Web Traffic from Commercial
Site (slide from Ronny Kohavi, Amazon)
Sept-11 Note significant drop in human traffic,
not bot traffic
Weekends
Internal Perfor-mance bot
Registration at Search Engine sites
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Robot / human identification

• Removal of robot data is important preprocessing
  step before clickstream analysis
• Robot page-requests often identified using a
  variety of heuristics
• e.g. some robots self-identify themselves in the
  server logs
• All robots in principle should visit robots.txt
  on the Web Server
• Also, robots should identify themselves via the
  User Agent field in page requests
• But other robots actively try to disguise that
  they are robots
• Patterns of access
• Robots explore the entire website in breadth
  first fashion
• Humans access web-pages more typically in
  depth-first fashion
• Timing between page-requests can be more regular
  for robots (e.g., every 5 seconds)
• Duration of sessions, number of page-requests per
  day often unusually large (e.g., 1000s of
  page-requests per day) for robots.
• Tan and Kumar (Journal of Data Mining and
  Knowledge Discovery, 2002) provide a detailed
  description of using classification techniques to
  learn how to detect robots

23
Fractions of Robot Data (from Tan and Kumar,
2002)
24
From Tan and Kumar, 2002Overallaccuraciesof
around 90were obtainedusing decisiontree
classifiers, trained on sessionsof lengths 1,
2, 3, 4,..
25
Page requests, caching, and proxy servers

• In theory, requester browser requests a page from
  a Web server and the request is processed
• In practice, there are
• Other users
• Browser caching
• Dynamic addressing in local network
• Proxy Server caching

26
Page requests, caching, and proxy servers
A graphical summary of how page requests from an
individual user can be masked at various stages
between the users local computer and the Web
server.
27
Page requests, caching, and proxy servers

• Web server logs are therefore not so ideal in
  terms of a complete and faithful representation
  of individual page views
• There are heuristics to try to infer the true
  actions of the user -
• Path completion (Cooley et al. 1999)
• e.g. If known B -gt F and not C -gt F, then session
  ABCF can be interpreted as ABCBF
• Anderson et al. 2001 for more heuristics
• In general case, it is hard to know what exactly
  the user viewed

28
Identifying individual users from Web server logs

• Useful to associate specific page requests to
  specific individual users
• IP address most frequently used
• Disadvantages
• One IP address can belong to several users
• Dynamic allocation of IP address
• Better to use cookies (or login ID if available)
• Information in the cookie can be accessed by the
  Web server to identify an individual user over
  time
• Actions by the same user during different
  sessions can be linked together

29
Identifying individual users from Web server logs

• Commercial websites use cookies extensively
• 97 of users have cookies enabled permanently on
  their browsers
• (source Amazon.com, 2003)
• However
• There are privacy issues need implicit user
  cooperation
• Cookies can be deleted / disabled
• Another option is to enforce user registration
• High reliability
• But can discourage potential visitors
• Large portals (such as Yahoo!) have high fraction
  of logged-in users

30
Sessionizing

• Time oriented (robust)
• e.g., by gaps between requests
• not more than 20 minutes between successive
  requests
• this is a heuristic but is a standard rule
  used in practice
• Navigation oriented (good for short sessions and
  when timestamps unreliable)
• Referrer is previous page in session, or
• Referrer is undefined but request within 10 secs,
  or
• Link from previous to current page in web site

31
Client-side data

• Advantages of collecting data at the client side
• Direct recording of page requests (eliminates
  masking due to caching)
• Recording of all browser-related actions by a
  user (including visits to multiple websites)
• More-reliable identification of individual users
  (e.g. by login ID for multiple users on a single
  computer)
• Preferred mode of data collection for studies of
  navigation behavior on the Web
• Companies like ComScore and Nielsen use
  client-side software to track home computer users

32
Client-side data

• Statistics like Time per session and Page-view
  duration are more reliable in client-side data
• Some limitations
• Still some statistics like Page-view duration
  cannot be totally reliable e.g. user might go to
  fetch coffee
• Need explicit user cooperation
• Typically recorded on home computers may not
  reflect a complete picture of Web browsing
  behavior
• Web surfing data can be collected at intermediate
  points like ISPs, proxy servers
• Can be used to create user profile and target
  advertise

33
Modeling Clickrate Data

• Data
• 200k Alexa users, client-side, over 24 hours
• ignore URLs requested
• goal is to build a time-series model that
  characterizes user click rates

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Markov-Poisson Model (Scott and Smyth, 2003)

• Doubly stochastic process
• Locally constant Poisson rate
• indexed by M Markov states
• Fit a model with M 3 states
• absence of a Web session
• Web session with slow click rate 1 minute rate
• Web session with rapid click rate 10 second rate
• Used hierarchical Bayes on individuals

39
Hierarchical Bayes Model
Population Prior p(lq)
l1 Individual 1
li Individual i
lN Individual N
D1
D1
D2
D3
D1
D2
Individuals with little data get shrunk to the
prior Individuals with a lot of data are more
data-driven
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Prediction with Hierarchical Bayes
Population Prior p(lq)
New Individual l ?
l1 Individual 1
lN Individual N
D1
D2
D3
D1
D2
First few clicks
Historical Training Data
42
Early studies from 1995 to 1997

• Earliest studies on client-side data are Catledge
  and Pitkow (1995) and Tauscher and Greenberg
  (1997)
• In both studies, data was collected by logging
  Web browser commands
• Population consisted of faculty, staff and
  students
• Both studies found
• clicking on the hypertext anchors as the most
  common action
• using back button was the second common action

43
Early studies from 1995 to 1997

• high probability of page revisitation
  (0.58-0.61)
• Lower bound because the page requests prior to
  the start of the studies are not accounted for
• Humans are creatures of habit?
• Content of the pages changed over time?
• strong recency (page that is revisited is usually
  the page that was visited in the recent past)
  effect
• Correlates with the back button usage
• Similar repetitive actions are found in telephone
  number dialing etc

44
The Cockburn and McKenzie study from 2002

• Earlier studies were outdates
• Web has changed dramatically in the past few
  years
• Cockburn and McKenzie (2002) provides a more
  up-to-date analysis
• Analyzed the daily history.dat files produced by
  the Netscape browser for 17 users for about 4
  months
• Population studied consisted of faculty, staff
  and graduate students
• Study found revisitation rates higher than past
  94 and 95 studies (0.81)
• Time-window is three times that of past studies

45
The Cockburn and McKenzie study from 2002

• Revisitation rate less biased than the previous
  studies?
• Human behavior changed from an exploratory mode
  to a utilitarian mode?
• The more pages user visits, the more are the
  requests for new pages
• The most frequently requested page for each user
  can account for a relatively large fraction of
  his/her page requests
• Useful to see the scatter plot of the distinct
  number of pages requested per user versus the
  total pages requested

46
The Cockburn and McKenzie study from 2002
The number of distinct pages visited versus page
vocabulary size of each of the 17 users in the
Cockburn and McKenzie (2002) study (log-log plot)
47
The Cockburn and McKenzie study from 2002
Bar chart of the ratio of the number of page
requests for the most frequent page divided by
the total number of page requests, for 17 users
in the Cockburn McKenzie (2002) study
48
Outline

• Basic concepts in Web log data analysis
• Predictive modeling of Web navigation behavior
• Markov modeling methods
• Analyzing search engine data
• Ecommerce aspects of Web log mining

49
Markov models for page prediction

• General approach is to use a finite-state Markov
  chain
• Each state can be a specific Web page or a
  category of Web pages
• If only interested in the order of visits (and
  not in time), each new request can be modeled as
  a transition of states
• Issues
• Self-transition
• Time-independence

50
Markov models for page prediction

• For simplicity, consider order-dependent,
  time-independent finite-state Markov chain with M
  states
• Let s be a sequence of observed states of length
  L. e.g. s ABBCAABBCCBBAA with three states A, B
  and C. st is state at position t (1lttltL). In
  general,
• first-order Markov assumption
• This provides a simple generative model to
  produce sequential data

51
Markov models for page prediction

• If we denote Tij P(st jst-1 i), we can
  define a M x M transition matrix
• Properties
• Strong first-order assumption
• Simple way to capture sequential dependence
• If each page is a state and if W pages, O(W2), W
  can be of the order 105 to 106 for a CS dept. of
  a university
• To alleviate, we can cluster W pages into M
  clusters, each assigned a state in the Markov
  model
• Clustering can be done manually, based on
  directory structure on the Web server, or
  automatic clustering using clustering techniques

52
Markov models for page prediction

• Tij P(st jst-1 i) represents the
  probability that an individual users next
  request will be from category j, given they were
  in category i
• We can add E, an end-state to the model
• E.g. for three categories with end state -
• E denotes the end of a sequence, and start of a
  new sequence

53
Markov models for page prediction

• First-order Markov model assumes that the next
  state is based only on the current state
• Limitations
• Doesnt consider long-term memory
• We can try to capture more memory with kth-order
  Markov chain
• Limitations
• Inordinate amount of training data O(Mk1)

54
Parameter estimation for Markov model transitions

• Smoothed parameter estimates of transition
  probabilities are
• If nij 0 for some transition (i, j) then
  instead of having a parameter estimate of 0 (ML),
  we will have allowing prior
  knowledge to be incorporated
• If nij gt 0, we get a smooth combination of the
  data-driven information (nij) and the prior

55
Parameter estimation for Markov models

• One simple way to set prior parameter is
• Consider alpha as the effective sample size
• Partition the states into two sets, set 1
  containing all states directly linked to state i
  and the remaining in set 2
• Assign uniform probability r/K to all states in
  set 2 (all set 2 states are equally likely)
• The remaining (1-r) can be either uniformly
  assigned among set 1 elements or weighted by some
  measure
• Prior probabilities in and out of E can be set
  based on our prior knowledge of how likely we
  think a user is to exit the site from a
  particular state

56
Predicting page requests with Markov models

• Deshpande and Karypis (2004) propose schemes to
  prune kth-order Markov state space
• Provide systematic but modest improvements
• Another way is to use empirical smoothing
  techniques that combine different models from
  order 1 to order k (Chen and Goodman 1996)

57
Mixtures of Markov Chains

• Cadez et al. (2003) and Sen and Hansen (2003)
  replace the first-order Markov chain
• with a mixture of first-order Markov chains
• where c is a discrete-value hidden variable
  taking K values Sk P(c k) 1 and
• P(st st-1, c k) is the transition matrix
  for the kth mixture component
• One interpretation of this is user behavior
  consists of K different navigation behaviors
  described by the K Markov chains

58
Modeling Web Page Requests with Markov chain
mixtures

• MSNBC Web logs
• Order of 2 million individual users per day
• different session lengths per individual
• difficult visualization and clustering problem
• WebCanvas
• uses mixtures of Markov chains to cluster
  individuals based on their observed sequences
• software tool EM mixture modeling
  visualization
• Next few slides are based on material in
• I. Cadez et al, Model-based clustering and
  visualization of navigation patterns on a Web
  site, Journal of Data Mining and Knowledge
  Discovery, 2003.

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From Web logs to sequences
128.195.36.195, -, 3/22/00, 103511, W3SVC,
SRVR1, 128.200.39.181, 781, 363, 875, 200, 0,
GET, /top.html, -, 128.195.36.195, -, 3/22/00,
103516, W3SVC, SRVR1, 128.200.39.181, 5288,
524, 414, 200, 0, POST, /spt/main.html, -,
128.195.36.195, -, 3/22/00, 103517, W3SVC,
SRVR1, 128.200.39.181, 30, 280, 111, 404, 3, GET,
/spt/images/bk1.jpg, -, 128.195.36.101, -,
3/22/00, 161850, W3SVC, SRVR1, 128.200.39.181,
60, 425, 72, 304, 0, GET, /top.html, -,
128.195.36.101, -, 3/22/00, 161858, W3SVC,
SRVR1, 128.200.39.181, 8322, 527, 414, 200, 0,
POST, /spt/main.html, -, 128.195.36.101, -,
3/22/00, 161859, W3SVC, SRVR1, 128.200.39.181,
0, 280, 111, 404, 3, GET, /spt/images/bk1.jpg, -,
128.200.39.17, -, 3/22/00, 205437, W3SVC,
SRVR1, 128.200.39.181, 140, 199, 875, 200, 0,
GET, /top.html, -, 128.200.39.17, -, 3/22/00,
205455, W3SVC, SRVR1, 128.200.39.181, 17766,
365, 414, 200, 0, POST, /spt/main.html, -,
128.200.39.17, -, 3/22/00, 205455, W3SVC,
SRVR1, 128.200.39.181, 0, 258, 111, 404, 3, GET,
/spt/images/bk1.jpg, -, 128.200.39.17, -,
3/22/00, 205507, W3SVC, SRVR1, 128.200.39.181,
0, 258, 111, 404, 3, GET, /spt/images/bk1.jpg, -,
128.200.39.17, -, 3/22/00, 205536, W3SVC,
SRVR1, 128.200.39.181, 1061, 382, 414, 200, 0,
POST, /spt/main.html, -, 128.200.39.17, -,
3/22/00, 205536, W3SVC, SRVR1, 128.200.39.181,
0, 258, 111, 404, 3, GET, /spt/images/bk1.jpg, -,
128.200.39.17, -, 3/22/00, 205539, W3SVC,
SRVR1, 128.200.39.181, 0, 258, 111, 404, 3, GET,
/spt/images/bk1.jpg, -, 128.200.39.17, -,
3/22/00, 205603, W3SVC, SRVR1, 128.200.39.181,
1081, 382, 414, 200, 0, POST, /spt/main.html, -,
128.200.39.17, -, 3/22/00, 205604, W3SVC,
SRVR1, 128.200.39.181, 0, 258, 111, 404, 3, GET,
/spt/images/bk1.jpg, -, 128.200.39.17, -,
3/22/00, 205633, W3SVC, SRVR1, 128.200.39.181,
0, 262, 72, 304, 0, GET, /top.html, -,
128.200.39.17, -, 3/22/00, 205652, W3SVC,
SRVR1, 128.200.39.181, 19598, 382, 414, 200, 0,
POST, /spt/main.html, -,
3
3
3
3
1
3
1
1
1
3
3
3
2
2
3
2
User 1
1
1
1
3
3
3
User 2
User 3
7
7
7
7
7
7
7
7
1
1
1
1
1
1
5
1
5
1
1
1
5
1
User 4
5
1
1
5
User 5


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Clusters of Finite State Machines
A
A
Cluster 1
Cluster 2
B
B
D
D
E
E
A
B
D
Cluster 3
E
62
Learning Problem

• Assumptions
• data is being generated by K different groups
• Each group is described by a stochastic finite
  state machine (SFSM)
• aka, a Markov model with an end-state
• Given
• A set of sequences from different users of
  different lengths
• Learn
• A mixture of K different stochastic finite
  state machines
• Solution
• EM is very easy fractional counts of transitions
• efficient and accurate, scales as O(KN)

63
Sketch of EM Algorithm for Mixtures of Markov
Chains

• Model mixture of K Markov chains (K fixed)
• Input data N categorical sequences (can be
  variable length)
• Initialization
• Generate random initial transition matrices for
  each of the K groups
• E-step
• Compute p( sequence i model k), for i1,..N, k
  1,K
• Use Bayes rule to compute p(model k sequence i)
• Yields membership probabilities for each sequence
• M-step
• Estimate the transition probabilities for each
  cluster, given membership probabilities
• Consists of fractional counting of transitions,
  
• e.g., sequence with probability 0.8 in cluster k,
  results in transition counts weighted by 0.8
• Repeat E and M steps until convergence
• Complexity of each iteration is O(K N L) where L
  is the average sequence length

64
Prediction with Markov mixtures
P(st1 s1,t )
65
Prediction with Markov mixtures
P(st1 s1,t ) S P(st1 , k s1,t )
S
P(st1 k , s1,t ) P(k s1,t )

66
Prediction with Markov mixtures
P(st1 s1,t ) S P(st1 , k s1,t )
S
P(st1 k , s1,t ) P(k s1,t )
S P(st1 k , st ) P(k
s1,t )
Prediction of kth component
Membership, based on sequence history
gt Predictions are a convex combination of K
different component transition matrices, with
weights based on sequence history
67
Experimental Methodology

• Model Training
• fit 2 types of models
• mixtures of histograms (multinomials)
• mixtures of finite state machines
• Train on a full days worth of MSNBC Web data
• Model Evaluation
• one-step-ahead prediction on unseen test data
• Test sequences from a different day of Web logs
• compute log P(users next click previous
  clicks, model)
• Using equation on the previous slide
• logP score
• Rewarded if next click was given high P by the
  model
• Punished if next click was given low P by the
  model
• negative average of logP scores predictive
  entropy
• Has a natural interpretation
• Lower bounded by 0 bits (perfect prediction)
• Upper bounded by log M bits, where M is the
  number of categories

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Timing Results
71
WebCanvas

• Software tool for Web log visualization
• uses Markov mixtures to cluster data for display
• extensively used within Microsoft
• also applied to non-Web data (e.g., how users
  navigate in Word, etc)
• Algorithm and visualization are in latest release
  of SQLServer (the sequence mining tool)
• Model-based visualization
• random sample of actual sequences
• interactive tiled windows displayed for
  visualization
• more effective than
• planar graphs
• traffic-flow movie in Microsoft Site Server v3.0

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Insights from WebCanvas for MSNBC data

• From msnbc.com site adminstrators.
• significant heterogeneity of behavior
• relatively focused activity of many users
• typically only 1 or 2 categories of pages
• many individuals not entering via main page
• detected problems with the weather page
• missing transitions (e.g., tech ltgt business)

74
Possible Extensions

• Adding time-dependence
• adding time-between clicks, time of day effects
• Uncategorized Web pages
• coupling page content with sequence models
• Modeling switching behaviors
• allowing users to switch between behaviors
• Could use a topic-style model users mixtures
  of behaviors
• e.g., Girolami M Kaban A., Sequential Activity
  Profiling Latent Dirichlet Allocation of Markov
  Chains, Journal of Data Mining and Knowledge
  Discovery, Vol 10, 175-196.

75
Related Work

• Mixtures of Markov chains
• special case Poulsen (1990)
• general case Ridgeway (1997), Smyth (1997)
• Clustering of Web page sequences
• non-probabilistic approaches (Fu et al, 1999)
• Markov models for prediction
• Anderson et al (IJCAI, 2001)
• mixtures of Markov outperform other sequential
  models for page-request prediction
• Sen and Hansen 2003
• Zukerman et al. 1999

76
Outline

• Basic concepts in Web log data analysis
• Predictive modeling of Web navigation behavior
• Markov modeling methods
• Analyzing search engine data
• Ecommerce aspects of Web log mining

77
Analysis of Search Engine Query Logs
78
Main Results

• Average number of terms in a query ranges from a
  low of 2.2 to a high of 2.6
• The most common number of terms in a query was 2
• The majority of users dont refine their query
• The number of users who viewed only a single page
  increased from 29 (1997) to 51 (2001) (Excite)
• 85 of users viewed only first page of search
  results (AltaVista)
• 45 (2001) of queries are about Commerce, Travel,
  Economy, People (was 20 in 1997)
• All four studies produced a generally consistent
  set of findings about user behavior in a search
  engine context
• most users view relatively few pages per query
• most users dont use advanced search features

79
Xie and O Halloran Study (2002)
- Query Length Distributions (bars) - Poisson
Model(dots lines)
80
Power-law Characteristics of Common Queries
Power-Law in log-log space

• Frequency f(r) of Queries with Rank r
• 110000 queries from Vivisimo
• 1.9 Million queries from Excite
• There are strong regularities in terms of
  patterns of behavior in how we search the Web

81
Outline

• Basic concepts in Web log data analysis
• Predictive modeling of Web navigation behavior
• Markov modeling methods
• Analyzing search engine data
• Ecommerce aspects of Web log mining

82
Ecommerce Data

• Page request Web logs combined with
• Purchase (market-basket) information
• User address information (if they make a
  purchase)
• Demographics information (can be purchased)
• Emails to/from the customer
• Search query information
• Product ratings information
• Main focus here is to increase revenue
• Data mining widely used by online commerce
  companies like Amazon
• This is a very rich source of problems for data
  mining
• What products should we advertise to this person?
• Can we do dynamic pricing?
• If a person buys X should we also suggest Y?
• Who are our best customers?
• etc
• Additional Reading

83
Predicting Purchase Behavior

• Can use predictive models, e.g., logistic
  regression, to try to predict on real-time if a
  customer will make a purchase or not
• Statistical models couple click-rate with
  purchase behavior
• Markov-type model through different states
• product viewing
• detailed product information
• reviews
• combine states with
• click rate and page content
• to predict p(purchase data up to time t)
• Reference Alan L. Montgomery, Shibo Li, Kannan
  Srinivasan, and John C. Liechty (2004), Modeling
  Online Browsing and Path Analysis Using
  Clickstream Data, Marketing Science, Vol. 23,
  No. 4, Fall 2004, p579-595.
• Potentially useful for ecommerce applications,
• e.g., real-time pricing/discounts
• but generally difficulty to predict if a customer
  will make a purchase or not

84
Recommender Systems

• Vote data n x m sparse binary matrix
• m columns products, e.g., books for purchase
  or movies for viewing
• n rows users
• Interpretation
• Implicit Ratings v(i,j) user is rating of
  product j (e.g. on a scale of 1 to 5)
• Explicit Purchases v(i,j) 1 if user i
  purchased product j
• entry 0 if no purchase or rating
• We will refer to non-zero entries generically as
  votes
• Automated recommender systems
• Given votes by a user on a subset of items,
  recommend other items that the user may be
  interested in

85
Examples of Recommender Systems

• Books and movies purchasing
• Amazon.com, Cdnow.com, etc
• Movie recommendations
• Netflix
• MovieLens (movielens.umn.edu)
• Digital library recommendations
• CiteSeer (Popescul et al, 2001)
• m 177,000 documents
• N 33,000 users
• Each user accessed 18 documents on average (0.01
  of the database -gt very sparse!)
• Web page recommendations
• E.g., Alexa toolbar (www.alexa.com)

86
Treatment of Zeros in Ratings Data

• Ratings data (e.g., rating movies on Netflix)
• User voluntarily assigns scores to movies viewed
• e.g., 5 for best and 1 for worst
• Interpretation of a score of 0
• The user has not seen this movie
• The user has seen the movie but has not rated it
• A 0 score is not necessarily the same as
  missing but often treated that way
• In much research work on recommender systems,
  ratings data is converted into binary votes
• e.g., ratings from gt3 mapped to a vote of 1, lt3
  mapped to 0
• Not ideal since now the 0 score can represent low
  ratings or unrated

87
Different recommender algorithms

• Nearest-neighbor/collaborative filtering
  algorithms
• Cluster-based algorithms
• Probabilistic model-based algorithms
• Details discussed in class.

88
Additional Aspects of Recommender Systems

• Dimension reduction
• Techniques like SVD can be used to perform
  predictions in a lower-dimensional space
• Content-based recommender systems
• In many cases there is additional information
  about the items
• E.g., reviews and synposes of movies
• A different approach to recommender algorithms is
  to make predictions on new items based on
  properties of rated items
• This approach can be combined with
  collaborative/user data
• Particularly useful (e.g.) when many items have
  no ratings
• e.g., Decoste et al (IUI, 2005) report that 85
  of movies have no ratings in a Yahoo! recommender
  system
• Additional data on users, e.g., demographic data
• May be useful, e.g., in clustering users
• Sequential aspect of recommendations
• e.g., novel Markov Decision Process approach by
  Shani et al, JMLR, 2005

89
General Issues

• The cold start problem
• How to make accurate recommendations for new
  users
• Sparsity of data
• Computational issues
• For real-time applications need to be able to
  make recommendations very quickly
• Significant engineering involved, many tricks
• Algorithm evaluation
• Not always clear what the evaluation metric
  (score) should be
• See next slide

90
Evaluation of Recommender Systems

• Research papers use historical data to evaluate
  and compare different recommender algorithms
• predictions typically made on items whose ratings
  are known
• e.g., leave-1-out method,
• each positive vote for each user in a test data
  set is in turn left out
• predictions on left-out items made given rated
  items
• e.g., predict-given-k method
• Make predictions on rated items given k1, k5,
  k20 ratings
• See Herlocker et al (2004) for detailed
  discussion of evaluation
• Approach 1 measure quality of rankings
• Score weighted sum of true votes in top 10
  predicted items
• Approach 2 directly measure prediction accuracy
• Mean-absolute-error (MAE) between predictions and
  actual votes
• Typical MAE on large data sets 20
  (normalized)
• E.g., on a 5-point scale predictions are within 1
  point on average

91
Evaluation of Recommender Systems

• Cautionary note
• It is not clear that prediction on historical
  data is a meaningful way to evaluate recommender
  algorithms, especially for purchasing
• Consider
• User purchases products A, B, C
• Algorithm ranks C highly given A and B, gets a
  good score
• However, what if the user would have purchased C
  anyway, i.e., making this recommendation would
  have had no impact? (or possibly a negative
  impact!)
• What we would really like to do is reward
  recommender algorithms that lead the user to
  purchase products that they would not have
  purchased without the recommendation
• This cant be done based on historical data alone
• Requires direct live experiments (which is
  often how companies evaluate recommender
  algorithms)

92
Additional Reading on Recommender Systems

• GroupLens research group, http//www.grouplens.org
  /
• Papers, demo systems, data sets
• Breese et al, Empirical analysis of predictive
  algorithms for collaboration filtering, 1998
• Schafer et al, Recommender systems in e-commerce,
  1999
• Sarwar et al, Analysis of recommendation
  algorithms for e-commerce, 2000
• Herlocker et al, Evaluating collaborative
  filtering recommender systems, ACM TOIS, 2004
• Shani et al, An MDP-based recommender system,
  2005