Transcript Data Mining for Web Personalization

Data Mining for Web
Personalization
Presented by the Highflyers group
Who are the Highflyers?
• Irfan Butt – Introduction and Traditional
approaches to Web Personalization
• Joel Gascoigne – Data Collection,
Preprocessing and Modelling
• James Silver – Pattern Discovery Predictive
Web User Modelling Part 1
• Aaron John-Baptiste – Pattern Discovery
Predictive Web User Modelling Part 2
• Asad Qazi – Evaluating Personalized Models
and Conclusion
Introduction
• Paper titled: Data Mining for Web
Personalization
• Author: Bamshad Mobasher
Irfan Butt
Introduction and Traditional approaches to Web
Personalization
Introduction to Web Personalization
• Personalization
▫ Delivery of content tailored to a particular user
• Web Personalization
▫ Delivery of dynamic content, such as text, links
tailored to a particular user or segments of user
Automatic Personalization Vs Customization
• Similarity: Both refer to delivery of content
• Difference: Creation and updating of user
profile
• Examples
▫ Customization: My Yahoo, Dell Website
▫ Automatic Personalization: Amazon
Personalization in Traditional Approaches
• Two phases in the process of personalization
1) Data Collection Phase
2) Learning Phase
• Classification based on learning from data
1. Memory Based Learning (Lazy)
▫ Examples: User-based collaborative system,
Content-based filtering system
2. Model Based Learning (Eager)
▫ Examples: Item-based System
Memory Based Learning VS Model Based Learning
• Memory Based Learning (Lazy)
▫ Huge memory required
▫ Scalability issue
▫ Adaptable to changes
• Model Based Learning (Eager)
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Limited memory required
Easily scalable
Learning phase offline
Not adaptable to changes
Traditional Approaches to Web
Personalization
• Rule Based Personalization Systems
▫ Rules are used to recommend item
▫ Rules based on personal characteristics of user
▫ Static profiles result in degradation of system
Traditional Approaches to Web
Personalization
• Content-based Filtering Systems
▫ User profile built on content descriptions of items
▫ Profile based on previous rating of items
Traditional Approaches to Web
Personalization
• Collaborative Filtering Systems
▫ Single profile is built in the same way i.e. contentbased filtering Systems
▫ Items from more than one profile is used to
recommend new item or content
▫ These profiles are K Nearest Neighbors based on
previous ratings of items of each profile
▫ Poor results as the system grows
Data Mining Approach to
Personalization
• Data Mining (or Web Usage Mining)
▫ The automatic discovery and analysis of patterns
in click stream and associated data collected or
generated as a result of user interactions with Web
resources on one or more Web sites
• Data Mining Cycle:
▫ Data preparation and transformation phase.
▫ Pattern discovery phase
▫ Recommendation phase
Joel Gascoigne
Data Collection, Preprocessing and Modelling
Data Modelling and Representation
• Assume the existence of a set of m users:
▫ U = {u1, u2, …, um}
• Set of n items:
▫ I = {in, in, …, in}
Data Modelling and Representation
• The profile for a user u є U is an n-dimensional
vector of ordered pairs:
▫ u(n) = {(i1, su(i1)), (i2, su(i2)), …, (in, su(in))}
• Typically, such profiles are collected over time
and stored
▫ Can be represented as an n x m matrix, UP
Data Modelling and Representation
• A Personalisation System, PS can be viewed as a
mapping of user profiles and items to obtain a
rating of interest
• The mapping is not generally defined for the
whole domain of user-item pairs
▫ System must predict interest scores
Data Modelling and Representation
• This general framework can be used with most
approaches to personalisation
• In the data mining approach:
▫ A variety of machine learning techniques are
applied to UP to discover aggregate user models
▫ These user models are used to make a prediction
for the target user
Data Sources for Web Usage Mining
• Main data sources used in web usage mining are
server log files
▫ Clickstream data
• Other data sources include the site files and
meta-data
Data Sources for Web Usage Mining
• This data needs to be abstracted
▫ Pageview
 Representation of a collection of web objects
▫ Session
 A sequence of pageviews by a single user
• All sessions belonging to a user can be
aggregated to create the profile for that user
Data Sources for Web Usage Mining
• Content data
▫ Collection of objects and relationships conveyed to
the user
 Text
 Images
▫ Also, semantic or structual meta-data embedded
within the site
 Domain ontology
 Could use an ontology language such as RDF
 Or a database schema
Data Sources for Web Usage Mining
• Also, operational databases for the site may
include additional information about user and
items
▫ Geographic information
▫ User ratings
Primary Tasks in Data Preprocessing for
Web Usage Mining
Data Preprocessing for Web Usage
Mining
• Goal:
▫ Transform click-stream data into a set of user
profiles
• This “sessionized” data can be used as the input
for a variety of data mining algorithms or further
abstracted
Data Preprocessing for Web Usage
Mining
• Tasks in usage data preprocessing:
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Data Fusion
Data Cleaning
Pageview Identification
Sessionization
Episode Identification
Data Preprocessing for Web Usage
Mining
• Data Fusion:
▫ Merging of log files from web and application
servers
• Data Cleaning:
▫ Tasks such as:
 Removing extraneous references to embedded
objects
 Removing references due to spider navigations
Data Preprocessing for Web Usage
Mining
• Pageview Identification:
▫ Aggregation of collection of objects or pages,
which should be considered a unit
▫ This process is dependent on the linkage structure
of the site
▫ In the simplets case, each HTML file has a one-toone correlation with a pageview
▫ Must distinguish between users
 Authentication system or cookies
Data Preprocessing for Web Usage
Mining
• Sessionization:
▫ Process of segmenting the user activity log of each
user into sessions, each representing a single visit
to the site
• Episode Identification:
▫ Episode is a subsequence of a session comprised
of related pageviews
Data Preprocessing for Web Usage
Mining
• These tasks ultimately result in a set of n
pageviews
▫ P = {p1, p2, …, pn}
• A set of v user transactions
▫ T = {t1, t2, …, tv}
• A user transaction captures the activity of a user
during a particular session
Data Preprocessing for Web Usage
Mining
• Finally, one or more transactions or sessions
associated with a given user can be aggregated to
form the final profile for that user
▫ If the profile is generated from a single session, it
represents short-term interests
▫ Aggregation of multiple sessions results in profiles
that capture long-term interests
Data Preprocessing for Web Usage
Mining
• The collection of these profiles comprises the m
x n matrix UP which can be used to perform
various data mining tasks
• After basic clickstream preprocessing steps, data
from other sources is integrated:
▫ Content, structure and user data
James Silver
Pattern Discovery Predictive Web User Modelling
Part 1
Model-Based Collaborative Techniques
• Two-stage recommendation process:
▫ (A) offline model-building (B) Real-time
scoring
(Explicit & Implicit user behavioural data used)
• Offline model-building algorithms:
(1) Clustering,
(2) Association Rule Discovery,
(3) Sequential Pattern Discovery,
(4) Latent Variable Models (part 2)
We also look at hybrid models (part 2)
(1) Clustering
• Clustering divides data into groups where:
▫ Inter-cluster similarities are minimised
▫ Intra-cluster similarities are maximised
• Generalization to Web usage mining
▫ User-based vs. Item-based clustering
▫ Efficiency and scalability improvements
(1) Clustering: User-based
• User profiles
• Partitions Matrix UP
▫ Clusters represent user segments based on
common navigational behaviour
• Recommendations (target user u, target item i)
▫ Centroid vector vk computed for each cluster Ck
▫ Neighbourhood: All user segments that have a
score for i and whose vk is most similar to u
(1) Clustering: Other
• Fuzzy Clustering
▫ Desirable to group users into many categories
• Distance issues
▫ Consider web-transactions as sequences
• Association Rule Hypergraph Partitioning
(ARHP)
(2) Association Rule Discovery
Finding groups of pages or items that are commonly
accessed or purchased together
• Originally for mining supermarket basket data
• Discovering Association Rules involves:
1)Discovering frequent itemsets
 Satisfying a minimum support threshold
2)Discovering association rules
 Satisfying a minimum confidence threshold
(2) Association Rules: Concepts
• Transactions set T
• Itemsets I = {I1,I2,...,Ik} over T
• Association rule r has the form X => Y (sr, cr)
▫ sr = the support of X U Y
(i.e. probability that X and Y occur together in a
transaction)
▫ cr = the confidence of the rule r
(i.e. the conditional probability that Y occurs in a
transaction, given that X has occurred in that transaction)
(2) Recommendations
• Matching rule antecedents with target user profiles
▫ Sliding window solution
▫ Naive approach
▫ Frequent Itemset Graph
• Finding Candidate pages:
▫ Match current user session window with previously
discovered frequent itemsets
• Recommendation Value
▫ Confidence of corresponding association rule
(2) Recommendations
(3) Sequential Models
• Now we consider the order when discovering
frequently occurring itemsets.
• So: given the user transaction {i1,i2,i3}
▫ Association rules (i1=>i2) and (i2=>i1) are fine
▫ But sequential pattern (i2=>i1) not supported
• Two types of sequences:
i3
▫ Contiguous (closed) sequence
▫ Open Sequence
{i1,i2,i4,i3}
• Frequent Navigational Paths
i1,i2 =>
{i1,i2,i3}
(3) Recommendations
• Trie-structure (aggregate tree)
▫ Each node is an item, root is the empty sequence
• Recommendation Generation
▫ Found in O(s) by traversing the tree
‘s’ = the length of the current user transaction deemed to be useful in
recommending the next set of items
▫ Sliding window w
 Maximum depth of tree therefore is |w|+1
▫ Controlling the size of the tree
(3) Sequential Models: Contiguous
• Contiguous sequence patterns are particularly
restrictive
▫ Valuable in page pre-fetching applications
▫ Rather than in general context of recommendation
generation
(3) Sequential Models: Markov
• Another approach for sequential modelling
▫ Based on Stochastic methods
• Modelling the navigational activity in the website
as a Markov chain
(3) Sequential Models: Markov
• A Markov model is represented by the 3-tuple
<A,S,T>
▫ A: set of possible actions (items)
▫ S: set of n states for which the model is built
(visitor’s navigation history)
▫ T=[pi,j]nxn: Transition Probability Matrix
 pi,j: probability of a transition from state si to state sj
• Order : Number of prior events used in
predicting each future event
(3) Markov for Web-mining
• Designed to predict the next user action based
on the user’s previous surfing behaviour
• Also used to discover high-probability user
navigational paths in a website
▫ User-prefered trails
• Various optimization methods
• Apart from Markov: Mixture Models
Aaron John-Baptiste
Pattern Discovery Predictive Web User Modelling
Part 2
(4) Latent Variable Models (LVMs)
• Latent Variables are variables that haven't been
directly observed but have rather been inferred.
▫ E.g. Morale is not measured directly but inferred
• Have more recently become popular as a
modelling approach in web usage mining
• Two commonly used LVMs
▫ Finite Mixture Models (FMM)
▫ Factor Analysis (FA)
(4) FA and FMM
• Factor Analysis
▫ Aims to summarise and find relationships within
observed data (all data)
▫ Used in pattern recognition, collaborative filtering
and personalization based web usage mining
• Finite Mixture Models (FMM)
▫ Use a finite number of components to model (a
page view, or user rating)
(4) Drawbacks to pure usage based
models
• Pure usage based models have drawbacks
▫ Process relies on user transactions or rating data
▫ New items or pages are therefore never
recommended (“new item problem”)
▫ Also do not use knowledge from underlying
domain and so cannot make more complex
recommendations
(5) Hybrid models
• Uses a combination of user-based and contentbased modelling.
• Three main types used in web mining
▫ Integrating content features
▫ Integrating semantic knowledge
▫ Using Linkage structure
(5) Integrating content features with
usage-based models
• Solves “new item problem”
▫ Use content characteristics of pages with userbased data
▫ Extract keywords from content to be used to
discover patterns
▫ Not just using user data means new pages with
relevant content can be recommended
▫ Users interests can be mapped to content,
(concepts or topics)
(5) Integrating structured semantic
knowledge with usage-based models
• Content feature integration is useful when pages
are rich in text and keywords
• However cannot capture more complex
relationships where items have underlying
properties
• Idea is to take the underlying meanings of
objects and add them to the user-based data.
Recommendations can then be made to pages or
items with similar semantic meanings
(5) Using Linkage structure for model
learning and selection
• Other semantic data can be used such as
relational databases and the hyperlink structure
on a web page
• Mobasher proposes a hybrid recommendation
system that switches between different
algorithms based on the degree of connectivity
in the site and user
• E.g. in a highly connected website, with short
paths, non sequential models performed better
Asad Qazi
Evaluating Personalized Models and Conclusion
Evaluating Personalization models
The Primary Goal of this section is to evaluate the
accuracy and effectiveness of web personalization
models
Why Evaluate?
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More complex web-based applications and more complex user interaction
requires the selection of more sophisticated models
Need to further explore the impact of recommended model on user
behaviour
There are several different modelling approaches to web personalization
Evaluating personalized models is an inherently challenging task firstly,
because different models require different evaluation metrics, secondly, the
required personalization actions may be quite different depending on the
underlying domain, relevant data and intended application
Finally, there is also a lack of consensus among researchers as to what
factors affect quality of service in personalized systems and of what
elements contribute to user satisfaction
Common evaluation approaches
• A number of metrics have been proposed in literature for
evaluating the robustness and predictive accuracy of a
recommender system: this includes
• Mean Absolute Error (MAE)
• Classification Metrics (Precision and Recall)
• Receiver Operating Characteristic (ROC)
• The use of business metrics to measure the customer loyalty
and satisfaction such as Recency Frequency Monetary (RFM)
• The use of other key dimensions along with metrics such as:
Accuracy, Coverage, Utility, Explainability, Robustness,
Scalability and User Satisfaction
Conclusions
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Web personalisation is viewed as an application of data mining which
dynamically
serves
customized
content
(pages,
products,
recommendations, etc.) to users based on their profiles, preferences, or
expected interests of data available to personalization systems, the
modelling approaches employed and the current approaches to
evaluating these systems
We have also discussed the various sources of data available to
personalization systems, the modelling approaches employed and the
current approaches to evaluating these systems
Recent user studies have found that a number of issues can affect the
perceived usefulness of personalization systems including, trust in the
system, transparency of the recommendation logic, ability for a user to
refine the system generated profile and diversity of recommendations
Most personalization systems tend to use a static profile of the user.
However user interests are not static, changing with time and context.
Few systems have attempted to handle the dynamics within the user
profile.
Any Questions?