Data Mining for Web Personalization Presented by the Highflyers group.
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Transcript of Data Mining for Web Personalization Presented by the Highflyers group.
![Page 1: Data Mining for Web Personalization Presented by the Highflyers group.](https://reader031.fdocuments.us/reader031/viewer/2022020800/55160e19550346a2308b51a7/html5/thumbnails/1.jpg)
Data Mining for Web PersonalizationPresented by the Highflyers group
![Page 2: Data Mining for Web Personalization Presented by the Highflyers group.](https://reader031.fdocuments.us/reader031/viewer/2022020800/55160e19550346a2308b51a7/html5/thumbnails/2.jpg)
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
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Introduction
•Paper titled: Data Mining for Web Personalization
•Author: Bamshad Mobasher
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Irfan Butt Introduction and Traditional approaches to
Web Personalization
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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
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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
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Personalization in Traditional Approaches
•Two phases in the process of personalization1) Data Collection Phase 2) Learning Phase
•Classification based on learning from data1.Memory Based Learning (Lazy)▫Examples: User-based collaborative system,
Content-based filtering system2.Model Based Learning (Eager)▫Examples: Item-based System
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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
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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
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Traditional Approaches to Web Personalization• Content-based Filtering Systems
▫User profile built on content descriptions of items
▫Profile based on previous rating of items
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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
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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
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Joel Gascoigne Data Collection, Preprocessing and
Modelling
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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}
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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
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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
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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
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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
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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
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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
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Data Sources for Web Usage Mining•Also, operational databases for the site
may include additional information about user and items▫Geographic information▫User ratings
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Primary Tasks in Data Preprocessing for Web Usage Mining
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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
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Data Preprocessing for Web Usage Mining•Tasks in usage data preprocessing:
▫Data Fusion▫Data Cleaning▫Pageview Identification▫Sessionization▫Episode Identification
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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
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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
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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
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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
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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
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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
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James SilverPattern Discovery Predictive Web User
Modelling Part 1
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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)
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(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
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(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
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(1) Clustering: Other
•Fuzzy Clustering▫ Desirable to group users into many
categories•Distance issues
▫Consider web-transactions as sequences
•Association Rule Hypergraph Partitioning (ARHP)
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(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 threshold2)Discovering association rules
Satisfying a minimum confidence threshold
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(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)
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(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
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(2) Recommendations
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(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: i1,i2 => i3▫ Contiguous (closed) sequence
{i1,i2,i3}▫ Open Sequence
{i1,i2,i4,i3}
•Frequent Navigational Paths
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(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
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(3) Sequential Models: Contiguous•Contiguous sequence patterns are
particularly restrictive▫Valuable in page pre-fetching applications▫Rather than in general context of
recommendation generation
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(3) Sequential Models: Markov
•Another approach for sequential modelling▫Based on Stochastic methods
•Modelling the navigational activity in the website as a Markov chain
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(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
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(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
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Aaron John-BaptistePattern Discovery Predictive Web User
Modelling Part 2
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(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)
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(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)
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(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
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(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
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(5) Integrating content features with usage-based models•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)
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(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
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(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
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Asad QaziEvaluating Personalized Models and
Conclusion
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Evaluating Personalization models
The Primary Goal of this section is to evaluate the accuracy and effectiveness of
web personalization models
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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 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
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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
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Conclusions
• 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.
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Any Questions?