Technologies for Web-based Adaptive Interactive Systems: - - PowerPoint PPT Presentation

Marios Belk

EPL344: Internet Technologies

Technologies for Web-based Adaptive Interactive Systems: Personalization Categories,

and Adaptation Mechanisms and Effects

Adaptation Component User Modeling Component

videos images text Decision Making & Adaptation Mechanisms Adaptive User Interface Usability User Experience

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High-level Adaptive and Interactive System Architecture

Agenda

• Underlying principles of adaptation and personalization from a technical and

design perspective

• Technical perspective

–

personalization categories

–

adaptation technologies for adapting content and functionality based on the characteristics of each user

–

how the Semantic Web and the Social Web contribute to AIS

• Design perspective

–

adaptation effects that are communicated to the user interface for adaptation and personalization systems

• Reference selected state-of-the-art adaptation and personalization systems and

frameworks

Personalization Categories

Link personalization Content personalization Personalized search Context personalization Authorized personalization Humanized personalization

Link Personalization

• Adaptation and personalization of the structure and

presentation of hyperlinks in an interactive system

• Achieved by selecting the links that are more relevant to the

user (e.g., based on interests, preferences), changing the

• riginal navigation space by reducing or improving the

relationships between nodes, and adapting the presentation

• f links
• Popular in

– E-Commerce – Educational Hypermedia Systems

Content Personalization

• Adapting and personalizing the content of the user interface
• Categories

– Node structure personalization entails filtering the content that is

relevant to the users, illustrating sections and information in which the users may be interested

– Node content personalization is finer grained than structure

personalization and involves adapting the information of the same node to various users

Personalized Search

• The process of tailoring and personalizing the search results to an

individual’s interests by taking into consideration information about the individual beyond the query provided

• Implemented on the server side as part of a search engine’s methods or
• n the client side on the user’s computer (e.g., as a plugin on the Web

browser).

• Two general approaches to personalize the Web search results

–

By modifying the user’s query

–

By re-ranking search results

Personalized Search

• Modeling users’ information for personalized Web search.

This can be achieved through the following techniques:

– Personalized search based on content analysis in which the system

compares and checks the content similarity between Web-pages and user models

– Personalized search based on hyperlink analysis in which the system

computes the personalized importance of Web documents for each user

– Personalized search based on collaborative approaches in which the

system presents similar search results to users with similar user models

Context Personalization

• Adaptation of information that is accessed in different

contexts of use

– User’s location – Interaction device – Physical environment or social context

• Example

– Text-recognition CAPTCHA mechanism may localize the text-based

challenge by presenting characters personalized to the users’ localized information

vs.

Authorized Personalization

• Applied when an interactive system provides different access of

information and action permission to users with different roles in the system.

• Role-based access control: access rights in particular sections of a

system are categorized under a role name. Most widely known approach

• Team-based Access Control: collaborative team work and incorporates

context information (i.e., the members of a team and the object instances) that is associated with collaborative tasks and accordingly applies this context information for access control

Humanized Personalization

• Aims at creating personalized user interfaces based on intrinsic human

factors

–

Emotional factors (anxiety, stress)

–

Personality traits

–

Cognitive styles

–

Learning styles

–

Visual attention

–

Elementary cognitive processing abilities, etc.

• Given the highly complex and multi-dimensional character of these

factors, personalizing content and functionality of interactive systems based on such human factors is still at its infancy and not yet widely applied in commercial interactive systems

Adaptation Mechanisms

Adaptation Mechanisms

• Implementation mechanisms to provide adaptation effects on

the user interface based on the user model

• Main adaptation mechanisms

– Basic adaptation mechanisms – User Customization – Rule-based mechanisms – Content-based mechanisms – Collaborative-based mechanisms – Web mining – Demographic-based filtering

Basic Adaptation Mechanisms

Count how many times a node has been accessed

User Customization

• The system provides an interface that allows users to construct a

representation of their own interests

• System is not considered adaptive but rather adaptable

–

But still this mechanism provides personalized content to the user

Rule-based Mechanisms

• System has rules to adapt content and functionality based on the user

model characteristics Online Banking System [USER].logged == False AND [USER].loginattempts.count > 2

Content-based Mechanisms

Extract keywords from documents the user has visited, bookmarked, saved, or explicitly provided Assign weights on each keyword indicating the importance in the user model Documents retrieved in response to search are also represented as a weighed vector of keywords Compare the profile vector with retrieved documents’ vectors

Create User Model

Golf 0.3 Surfing 0.9 N top most frequently appeared keywords are included in the profile Golf 0.2 Surfing 0.6 Football 0.4

Search Adaptation Process

Display documents that are similar to the user model

Suggest the user links to a specified page by analyzing page content

Collaborative-based Mechanisms

• Based on the assumption that if users X and Y rate n items similarly,
• r have similar behaviors (e.g., buying, watching), and hence will

have similar interests

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Web Mining

• Web mining includes data mining techniques with the aim to

identify patterns from Web systems

• Main categories

– Web content mining which aims at the extraction and integration of

data and knowledge from Web-page content

– Web-structure mining which aims at the analysis of node and

connection structure of a Web-site

– Web usage mining which aims at extracting useful information from

server logs about the interaction activity of users, e.g., discover what users are looking for in a Web-page

Web Mining

• Applies statistical and data mining techniques on server log data, resulting in a set
• f useful patterns that indicate users’ navigational behavior. Given the site map

structure and usage logs, a Web usage miner provides results regarding usage patterns, user behavior, session and user clusters, clickstream information, etc.

• Data mining methods employed

–

Association rule mining

–

Sequential pattern discovery

–

Clustering

–

Classification

• Web Mining process

–

Pre-processing and data preparation, including data cleaning, filtering, and transaction identification, resulting in a user transaction file

–

Data mining step in which usage patterns are discovered via specific usage mining techniques

Demographic-based Filtering

• Complements other adaptation mechanisms such as rule-based and

collaborative filtering, aiming to refine the personalization result

• Utilizes demographic information of users (e.g., age, gender, profession,

etc.) to infer users’ interests and accordingly recommend particular

• bjects
• This method uses demographic information to identify the types of users

that prefer a certain object and to identify one of the several pre-existing clusters to which a user belongs aiming to tailor recommendations based

• n information about others in this cluster

Semantic Web Technologies for AIS

• Necessity to study and design the structure of meta-data

(semantics) coming from the provider’s side

• Aim: feed the adaptation mechanism with semantically

enriched, machine-understandable information in order to adapt the hypermedia content based on the users’ models

Ontologies

• Ontologies are widely used to organize and give meaning to information.
• Ontologies formally define the types, properties and interrelationships of
• the main entities of an interactive system
• SHOE: A rich and powerful representation ontology. A set of Simple HTML

Ontology Extensions that allow Web authors to annotate their pages with semantics expressed in terms of ontologies

• SHOE provides the ability to define ontologies, create new ontologies

which extend existing ontologies, and classify entities under an “is a” classification scheme

The Semantic Web Initiative

• Focuses on the creation of technologies and ontology languages and use of richer
• ntologies that can capture
• a wider variety of relationship types that will facilitate machines to understand the

semantics, or meaning, of information on the Web

• Ontology representation languages

–

SHOE

–

Extensible Markup Language (XML)

–

Resource Description Framework (RDF)

–

DAML + OIL

–

Web Ontology Language (OWL)

• High profile service providers such as Google utilize Semantic Web technologies

by using RDFa and Microformats embedded in XHTML ( 2015 ), with the aim to support enhanced searching in Web-pages

–

Usage example: Google states that the extra (structured) data will be used in order to get results for product reviews (e.g., CNET Reviews), products (e.g., Amazon product pages), and people (e.g., LinkedIn profiles)

Social Web for AIS

• Researchers have investigated the effects of personality traits on human

behavior in social networks or for implicitly eliciting personality traits based on social interaction data

• Examples

–

Wald et al. (2012) utilized data mining and machine learning techniques to predict users’ personality traits based on the Big Five personality model, using demographic and text-based attributes extracted from the users’ Facebook profiles

–

Ortigosa et al. (2014) proposed an automated approach for predicting users’ personality traits based on Facebook usage

• Facebook application which has been used in a large scale study ( N = 20,000) to

collect information about the personality traits of users and their interactions within Facebook

• Classifiers have been trained aiming to identify interaction patterns (e.g., number of

friends, number of wall posts) that relate to (and eventually predict) users’ personality traits

Adaptation Effects

Interactive System Architecture - What to Adapt?

Interactive Systems Information Architecture Functionality Content Content Presentation Content Navigation

What to Adapt?

• An important adaptation issue is which visible features of the system can be

adapted by a particular technique

• Two main classes of adaptation technologies

–

content-level adaptation, called adaptive content presentation

–

link-level adaptation, called adaptive navigation support

• Adaptive presentation relates to the adaptation of hypermedia elements inside

nodes

• Adaptive content navigation support relates to the adaptation of links inside nodes,

indexes and maps

Adaptation Effects in UIs Content-level adaptation

Adapt the hypermedia elements (or content fragments) of a node

Link-level adaptation

Adapt the presentation of hyperlinks within a node in order to support user navigation in the hyperspace

Content-level Adaptation

<SELECT statement> ::= [WITH <common_table_expression> [,...n]] <query_expression> [ ORDER BY {

• rder_by_expression | column_position [ ASC | DESC ]

} [ ,...n ] ] [ COMPUTE { { AVG | COUNT | MAX | MIN | SUM } (expression )} [ ,...n ] [ BY expression [ ,...n ] ] ] [ <FOR Clause>] [ OPTION ( <query_hint> [ ,...n ] ) ] <query_expression> ::= { <query_specification> | ( <query_expression> ) } [ { UNION [ ALL ] | EXCEPT | INTERSECT } <query_specification> | ( <query_expression> ) [...n ] ] <query_specification> ::= SELECT [ ALL | DISTINCT ] [TOP ( expression ) [PERCENT] [ WITH TIES ] ] < select_list > [ INTO new_table ] [ FROM { <table_source> } [ ,...n ] ] [ WHERE <search_condition> ] [ <GROUP BY> ] [ HAVING < search_condition > ]

Link-level Adaptation

User Features

Knowledge, Background, Individual Traits, Location Interests Knowledge, Background, Individual Traits Knowledge, Background, Goals, Individual Traits Interests, Location

Domain

Educational Hypermedia Systems News systems, Online Commercial Shops Educational Hypermedia Systems Educational Hypermedia Systems, Web‐based systems Web Recommender systems

Aim

Guide Reduce navigation time and steps Reduce cognitive load Support navigation Reduce navigation time and steps