Personalization

Personalization User Attitudes Regarding a User-Adaptive eCommerce Web Site Personalizing the User Experience on ibm.com Impacts of User Privacy Preferences on Personalized Systems – a Comparative Study Frans Faizal ffaizal@ics.uci.edu ICS 206 Spring 2003

Personalization User Attitudes Regarding a User-Adaptive eCommerce Web Site Personalizing the User Experience on ibm.com Impacts of User Privacy Preferences on Personalized Systems – a Comparative Study Frans Faizal (ffaizal@ics.uci.edu)

Overview • Describes user studies that focused on the perceived value of a variety of personalization features for an eCommerce Web site for computing machinery sales and support. • Describes how the results of the studies affect the design of user-adaptive applications. Frans Faizal (ffaizal@ics.uci.edu)

Definitions • Personalization • The use of information about a particular user to provide tailored (personalized) user experiences for that user. • A personalized Web site • A system that adapts the content structure, and/or presentation of the networked hypermedia objects to each individual user’s characteristics, usage behavior, and/or usage environment. Frans Faizal (ffaizal@ics.uci.edu)

Overview of User Studies (1) • Purpose • To determine which specific personalization features would be judged the most usable, valuable, and attractive to users of an eCommerce Web sites. • Gathered a large amount of quantitative and qualitative data. • Written and spoken opinions, written questionnaires, think aloud protocols, free-form group and one-on-one discussions, as well as direct observations. Frans Faizal (ffaizal@ics.uci.edu)

Overview of User Studies (2) • Obtained clear attitudes of users toward adaptive techniques that were intrinsic to the implementation and design of the personalization features being tested. • Conducted three studies, carried out in multiple laboratory settings. • Each has different participants and different methodologies (group vs. individual study). Frans Faizal (ffaizal@ics.uci.edu)

Personalization Feature Space • Started with 75 techniques (clustered based on similarities). • Wanted to refine the list based on measure of effectiveness, usefulness, and user attitudes derived from successive user studies. Frans Faizal (ffaizal@ics.uci.edu)

Frans Faizal (ffaizal@ics.uci.edu)

Prototype Adaptive Web Site (1) • Two prototype systems: low-fidelity (Study 1 & 2) and interactive versions (Study 3). • Implemented in Microsoft PowerPoint and presented on an IBM ThinkPad computer. • Low-fidelity prototype consisted of screen shots. • Lead experimenter clicked on a widget and the response was displayed on the screen. Frans Faizal (ffaizal@ics.uci.edu)

Prototype Adaptive Web Site (2) • Designed to demonstrate specific personalization and adaptive features. • Exemplified a Web site (a system) that maintains a profile of the user’s personal information and tailors the site’s content to that user based on the profile and navigational context. • PersonalBook • Central personalization tool that is closely tied to user profile. Frans Faizal (ffaizal@ics.uci.edu)

Written Questionnaires (1) • Used to capture both quantitative and qualitative data. • Also asked subjects to rate the personalization features demonstrated in each study. • Stated as assertions. • E.g., you control all the data kept in your profile and can review and edit it at any time. Frans Faizal (ffaizal@ics.uci.edu)

Written Questionnaires (2) • In Study 1, participants were asked to rank the features shown based on their value to the participants. • In Study 2 & 3, they were asked to rate the features using a 7-point scale (1 is “Highly Valuable”, 7 is “Not at all Valuable”). • Questionnaires also asked marketing and business case issues (whether subjects thought they would be more likely to come back and buy more). Frans Faizal (ffaizal@ics.uci.edu)

User Task Scenario “You and your department have made various server, laptop, and desktop purchases. You now think you may have to purchase additional memory to enhance the capabilities of the laptops used by your department members. Starting from your PersonalBook, find 128MB add-on memory chipsets compatible with those laptops. Then also find memory compatible with the desktop machines your department owns…” Frans Faizal (ffaizal@ics.uci.edu)

Results and Conclusion (1) • Users want to be in control of their personal information. • Able to review, modify, and delete personal information in their profile. • Able to control over who sees and uses the information. • Do not want their information gathered implicitly. • Able to decide which information to be stored in their profile. Frans Faizal (ffaizal@ics.uci.edu)

Results and Conclusion (2) • Users want to be in control of the content shown on a site. • Seems to defeat the purpose of an adaptive site. • They are happy as long as the content is generated based on the information they provide explicitly to the system. • E.g. content-filtering and content-refinement in the PersonalBook. Frans Faizal (ffaizal@ics.uci.edu)

Results and Conclusion (3) • Adapting content based on implicit information is undesirable. • E.g. “Compatible Memory” scenario. • Adapting content based on past navigation is also undesirable. • You can’t do it well, so don’t do it at all. • Users want to be “invisible” during exploratory sessions. • I.e. multiple user roles or persona. Frans Faizal (ffaizal@ics.uci.edu)

Results and Conclusion (4) • Adapting content based on transient information is good as long as it is clear what is going on. • Collaborative filtering was not supported fully. • “I am not like other people. I have different needs.” • Inappropriate products or services? Frans Faizal (ffaizal@ics.uci.edu)

Questions/Comments? Frans Faizal (ffaizal@ics.uci.edu)

Overview • Describes a strategy for bringing personalization to the ibm.com public Web site. Frans Faizal (ffaizal@ics.uci.edu)

Definitions (1) • Personalizing interaction • The use of information about a user to alter the content and functionality of the user experience. • Personalizing a Web site • Using personal information about an individual to tailor the experience for that individual on the site. Frans Faizal (ffaizal@ics.uci.edu)

Definitions (2) • Personalization policy • A decision made by an eCommerce company involving the handling of personal data on the company’s Web site. • Personalization feature • A method for collecting and using personal information in order to tailor a Web site visitor’s experience on the Web site. • A personalization policy applies to the whole Web site, while a feature provides functionality for a particular task on the site. Frans Faizal (ffaizal@ics.uci.edu)

Personalization for eCommerce • Involving customer and provider (producer) roles that interacts with each other. • The goal is to provide increased interaction value to both parties using their personal information. • Value of customer • F(cost of providing info, perceived benefits) • Value of company (provider) • F(cost of gathering info, perceived value) Frans Faizal (ffaizal@ics.uci.edu)

Personalization Value Space • A range of information type and possible values to customers and businesses. • The value of techniques to any customer will vary with the role of the customer at any time. • The value of a technique to a business will depend on the kind of business objective they have. • There are likely to be interactions between techniques resulting in a package of techniques that would be optimally effective. Frans Faizal (ffaizal@ics.uci.edu)

Project Goals • To understand the value of personalization to customers and IBM. • To develop the strategy for bringing personalization to the ibm.com public Web site which ensures that the top-priority goals of customers and the business are met. Frans Faizal (ffaizal@ics.uci.edu)

Project Approach (1) • Completing a literature review of the published research in the area of personalization. • Identify possible personalization features and understand state of the art. • Completing a set of heuristic evaluations of the ibm.com site and key competitors to understand current best practices. • Dell, HP, Compaq, IBM, Sun, and Amazon Frans Faizal (ffaizal@ics.uci.edu)

Project Approach (2) • Identify business requirements • Done primarily by ibm.com stakeholders. • Gathering information about personalization features that might be used. • Came up with 75 features (as described in the previous paper) and three policies (described next). • Executing iterative user studies. Frans Faizal (ffaizal@ics.uci.edu)

Three Policies (1) • Giving Web site visitors control of the data in their profiles. • They can review, edit, or delete information about themselves, their purchase, etc. • Asking visitors for the minimal amount of personal information necessary and providing immediate value to the customer based on use of it (Permission Marketing). • The customer’s profile is built slowly over time as the individual develops trust in the eCommerce company. Frans Faizal (ffaizal@ics.uci.edu)

Three Policies (2) • Enabling Web site visitors to adopt different level of identities as appropriate to their tasks on the Web site. • Level of identity is based on degree of personal information provided. • If no information is given, the visitor is invisible. Frans Faizal (ffaizal@ics.uci.edu)

Questions/Comments? Frans Faizal (ffaizal@ics.uci.edu)

Overview • Compares 30 opinion surveys on Internet privacy, categorizes the responses, and matches them with possible impacts on personalized systems. • A first contribution towards the identification of requirements for privacy-preserving personalization, to improve users’ trust when interacting with personalized systems. Frans Faizal (ffaizal@ics.uci.edu)

What is Personalization? • Personalization is predictive analysis of consumer data used to adapt targeted media, advertising, or merchandising to consumer needs. • A personalized hypermedia application is a hypermedia system which adapts the content, structure and/or presentation of the networked hypermedia objects to each individual user’s characteristics, usage behavior and/or usage environment. Frans Faizal (ffaizal@ics.uci.edu)

User-Adaptable vs. User-Adaptive Systems • User-adaptable systems • User is in control of the initiation, proposal, selection, and production of the adaptation. • User-adaptive systems • Performs all steps autonomously. • E.g., Amazon.com. • Generates purchase recommendations based one a user’s purchase and interaction history. Frans Faizal (ffaizal@ics.uci.edu)

Advantages of Personalization • Enables online sites to offer more relevant content and to recall user preferences and interests. • Improves the learning progress in educational software. Frans Faizal (ffaizal@ics.uci.edu)

Privacy-Critical Personalization Processes (1) • Personalization • Recurring processes of data collection, profiling, and matching. • From the collected data, user profiles are created and used to personalized contents. • Then, new data are collected, and profiles are updated. Frans Faizal (ffaizal@ics.uci.edu)

Privacy-Critical Personalization Processes (2) • Data collection • The most privacy-critical in the personalization process. • Could provoke privacy fears that limit consumers’ willingness to share information. Frans Faizal (ffaizal@ics.uci.edu)

Data Types (1) • User data • Information about personal characteristics of the user. • E.g., demographic data and user knowledge, skills, capabilities, interests, preferences, goals, and plans. Frans Faizal (ffaizal@ics.uci.edu)

Data Types (2) • Usage data • Related to users’ interactive behavior. • E.g., selective actions, temporal viewing behavior, ratings, purchases and purchase-related actions, and other confirmatory and disconfirmatory actions. Frans Faizal (ffaizal@ics.uci.edu)

Data Types (3) • Usage regularities • Based on frequently re-occurring interactions of users. • E.g., usage frequency, situation-action correlation, and action sequences. • Environment data • Focuses on the user’s software and hardware and the characteristics of the user’s current locale. Frans Faizal (ffaizal@ics.uci.edu)

Privacy Surveys • Looked at 30 surveys (or summary of survey) from 2001-2002. • Eleven included all questions (full reports). • Six provided an extensive discussion of survey results (elaborate executive summaries). • Ten gave factual executive summaries. • Three were only available in a form of press releases. Frans Faizal (ffaizal@ics.uci.edu)

Different Aspects of Privacy (1) • Privacy of personal information in general • User statements addressing this aspect have a direct impact on personalized systems requiring personal information. • E.g., statements regarding security of providing personal and sensitive information and sharing of such information. Frans Faizal (ffaizal@ics.uci.edu)

Different Aspects of Privacy (2) • Privacy of personal information in a commercial context. • User statements addressing this aspect primarily affect eCommerce in general and specifically personalized systems in an eCommerce environment. • E.g., statements regarding security and sharing of personal information given during an online transaction. Frans Faizal (ffaizal@ics.uci.edu)

Personalization