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Data Mining for Web Personalization


Examples: User-based collaborative system, Content-based filtering system ... Examples: Item-based System. Memory Based ... Data Mining (or Web Usage Mining) ... – PowerPoint PPT presentation

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Title: 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

  • Paper titled Data Mining for Web Personalization
  • Author Bamshad Mobasher

  • Irfan Butt
  • Introduction and Traditional approaches to Web

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
  • 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
  • Memory Based Learning (Lazy)
  • Examples User-based collaborative system,
    Content-based filtering system
  • 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)
  • 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
  • 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.
    content-based 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 ? U is an n-dimensional
    vector of ordered pairs
  • u(n) (i1, su(i1)), (i2, su(i2)), , (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

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
  • Geographic information
  • User ratings

Primary Tasks in Data Preprocessing for Web Usage
Data Preprocessing for Web Usage Mining
  • Goal
  • Transform click-stream data into a set of user
  • 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
  • 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
  • Data Cleaning
  • Tasks such as
  • Removing extraneous references to embedded
  • 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-to-one 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
  • 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
  • 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
  • Discovering frequent itemsets
  • Satisfying a minimum support threshold
  • 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 gt 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

(2) Recommendations
  • Matching rule antecedents with target user
  • 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 (i1gti2) and (i2gti1) are fine
  • But sequential pattern (i2gti1) not supported
  • Two types of sequences i1,i2 gt i3
  • Contiguous (closed) sequence i1,i2,i3
  • Open Sequence i1,i2,i4,i3
  • Frequent Navigational Paths

(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 trees 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 w1
  • Controlling the size of the tree

(3) Sequential Models Contiguous
  • Contiguous sequence patterns are particularly
  • Valuable in page pre-fetching applications
  • Rather than in general context of recommendation

(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 set of possible actions (items)
  • S set of n states for which the model is built
    (visitors navigation history)
  • Tpi,jnxn 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 users 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
  • 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
  • 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
  • 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
    content-based modelling.
  • Three main types used in web mining
  • Integrating content features
  • Integrating semantic knowledge
  • Using Linkage structure

(5) Integrating content features with usage-based
  • Solves new item problem
  • Use content characteristics of pages with
    user-based 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
  • 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?
  • 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
  • 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
  • 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

  • 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?