User interface adaptation based on user feedback and machine learning
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Proceedings of the companion publication of the 2013 international conference on Intelligent user interfaces companion
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User interface adaptation based on user feedback and machine learning
- 1. Presentation Outline Motivation Basic concept Bakground Futur work Conclusion Nesrine MEZHOUDI nesrine.mezhoudi@uclouvain.be User Interface Adaptation Based on User Feedbacks and Machine Learning Louvain Interaction Lab Université catholique de Louvain Promoter: Prof. Jean Vanderdonckt Jean.vanderdonckt@uclouvain.be 1
- 2. Adaptation & User Centered Design 2 Not adapted & not User-centered UI
- 3. Adaptation & User Centered Design 3 adapted & not User-centered UI
- 4. Adaptation & User Centered Design 4 Adapted & User-centered UI
- 5. Outline 5 Motivation Basic concepts Methods & Application
- 6. Adaptation rules are static 6 Adaptation rules are implemented according to a predefined static set of standards, guidelines, and recommendations Hardly re-adaptable Barely impossible to update Highly expensive (redevelopment, time, human resources)
- 7. Static rules prevent adaptation 7 • Dissatisfaction • Frustration • Discouragement • Loss of motivation • …
- 8. Solution: Interaction-based adaptation 8 Objective: enhancing the end-user influence in the UI definition
- 9. 99
- 10. Principals typologies to express feedback 10 Implicit Feedback Explicit Feedback Without rating aims With rating aims 153 Fig. 21. Emoticon answers with an argument. During this two-week experiment the participants got 2–4 automatic (multiple choice or emoticons) questions per day (in total 28 questions) via mobile phone with sound alarm. Both experiments illustrate that this Mobile Feedback method is very fast and easy to use for users but after while it starts to annoy because it interrupts the user unnecessarily. In this version we improved the emoticon set (Fig. 22) by taking Sleepy emoticon off and leaving the middle place empty. When the user got a question the cursor was in the middle, and this empty place made sure that the user did not select an emoticon by chance just by pressing the cursor once. Now he had to move the cursor and then make a selection. Fig. 22. Descriptions of emoticons. 6.1.4 Evaluation of the electronic Experience-Diary In this case, we also wanted to use a diary in order to get more in-depth experiences than just using the mobile feedback method. Based on lessons learnt from case 3, we did not give the users a paper diary, because we wanted to control Humor Neutral Distressed Happy Superhappy Amazed Furious Angry 10
- 11. Unified theoretical architecture for adaptation based on ML 11 Context • User • Platform • Environment Perception (trackingtools,sensors…) Recommendation Feedback ReinforcementEvaluation UI
- 12. The Adaptation Rule Manager 12 Adaptation Rules Repository Trainer-Rule Engine Learner-Rule Engine Adaptation Rules Manager Generated Rules Rule Engine Rule Management Tools Training Rules Feedback s User
- 13. The Adaptation Rule Manager 13 Adaptation Rules Repository Trainer-Rule Engine Learner-Rule Engine Adaptation Rules Manager Generated Rules Rule Engine Rule Management Tools Training Rules Feedback s User (1) Executing pre-existed adaptation rules, serving as a training set to (2) detect a pattern of user behavior throughout his feedbacks. Besides, (3) coming up with statistics and (promote/demote) ranking for the Learner Rule Engine (RLE).
- 14. The Adaptation Rule Manager 14 Adaptation Rules Repository Trainer-Rule Engine Learner-Rule Engine Adaptation Rules Manager Generated Rules Rule Engine Rule Management Tools Training Rules Feedback s User analyzing collected user judgments. Which are intended to serve in a promoting/demoting ranking, Then generate new decision rules , (Learns)
- 15. Applications 15 learning based adaptation to improve the validity o Abstract User Interface. Several algorithms were de for the AUI definition however a lack of validity consistency control still arise (figure3). Figure 3. A tasks grouping sample Tasks AUI CUI Final UI
- 16. Applications 16 techniques were already explored [5], ML still promising t emphases new adapting scenario for widget selection in th concrete UI, which is full of adaptation rules and guideline In figure 4 we show a sample for considering adaptatio guidelines to select a multiple-choice widget selection. Figure 4. A Multiple-choice widgets definition for a known domain Tasks AUI CUI Final UI
- 17. Conclusion 17 State of the arts Conceptualization Implantation Test & Evaluation
- 18. Thank you for your attention. 18 Nesrine Mezhoudi
Editor's Notes
- Welcome to my presentation about user interface adaptation based on users feedbacks and machine learning, my name is nesrine mezhoudi , I’m a PHD student and a research assistant in the catholic university of Louvain , I’m investigating the potential of machine learning for adaptive user interface and specially model based User Interfaces.
- Nowadays, Context ware user interface, wish denote the UI adapted to their context of use , are holding a big interest. given that the context of use is defined by the triplet<user, platform, environment> ; user, denoting the end user, platform representing the device holding the user interface and the environment in wish the interaction is taking place. currently , current systems migrate the adaptation of user interface into more user centered aspect focusing on the end user preferences and aiming at increasing their usability as well as their user satisfaction and user experiences.
- Nowadays, Context ware user interface, wish denote the UI adapted to their context of use , are holding a big interest. given that the context of use is defined by the triplet<user, platform, environment> ; user, denoting the end user, platform representing the device holding the user interface and the environment in wish the interaction is taking place. currently , current systems migrate the adaptation of user interface into more user centered aspect focusing on the end user preferences and aiming at increasing their usability as well as their user satisfaction and user experiences.
- Nowadays, Context ware user interface, wish denote the UI adapted to their context of use , are holding a big interest. given that the context of use is defined by the triplet<user, platform, environment> ; user, denoting the end user, platform representing the device holding the user interface and the environment in wish the interaction is taking place. currently , current systems migrate the adaptation of user interface into more user centered aspect focusing on the end user preferences and aiming at increasing their usability as well as their user satisfaction and user experiences.
- I’m going to start by giving an overview about this presentation, it is structured as follows , First I’m going to introduce the concepts of adaptive user interfaces and contextualization, beside the context of use and adaption which are considered the key points for a user centered design . Then I will put in evidence the weakness of existing works considered as the main motivation for the current investigation of machine learning techniques for user interface adaptation. Later we introduce the basic concepts and the background of the works before outlining the approach and its forseen application Finally I will conclude and give the outlooks of this investigation.
- Commonly Adaptive user interface are based on a set of predefined and even static adaptation rules defined regarding standards, ergonomic guidelines, esthetic metrics… Accordingly it is hard to readapt the UI neither to update the existing adaptation rules wish supposed to evolve across time. and even it it is possible in what costs ? this lack of dynamic adaptability among systems has several consequences: user dissatisfaction because these systems do not fit the users needs, limitations, and preferences, user demotivation to use the system and finally user withdrawal Thereby the dynamicity and the interactivity od adaptation began a crucial requirement to keep the user and ensure their satisfaction.
- Commonly Adaptive user interface are based on a set of predefined and even static adaptation rules defined regarding standards, ergonomic guidelines, esthetic metrics… Accordingly it is hard to readapt the UI neither to update the existing adaptation rules wish supposed to evolve across time. and even it it is possible in what costs ? this lack of dynamic adaptability among systems has several consequences: user dissatisfaction because these systems do not fit the users needs, limitations, and preferences, user demotivation to use the system and finally user withdrawal Thereby the dynamicity and the interactivity od adaptation began a crucial requirement to keep the user and ensure their satisfaction.
- Thus , systems need to converge to advisory software considering the user intervention as a highly valuable factor for adaptation. Accordingly , Interaction shouldn’t be overlooked (neglected ) as a factor for adaptation as well as the context of use in order to get a more appropriate user adaptation. Wish seems promising to enhance the users preferences influence in the user interface definition and adaptation moreover for the update of the adaptation rules set and their management. To meet such need Machine learning techniques seems potential and promising .
- During this cycle user behaviors and intervention provide a rich source improving the quality of adaptation , usually user feedbacks are classified into implicit and explicit feedbacks The implicit one consider that every interaction as an interest indicator but those feedbacks class still enable to to illustrate a dislike attitude of users. The explicit feedback consist in acquiring knowledge by asking user in more expressive way . Here we present the principal topologies to express feedback which consider mainly too axes implicit/explicit and with or without rating aims .
- With regard to above mentioned cycle , we propose a theoretical architecture unifying machine learning based adaptive systems . it convey the main modules unifying the conceptual description of various possible instantiation. A ml based Adaptive user interface is mainly structured around an instantiated context of use( user, on a platform in an environment). Beside it focuses on the exploration of the interaction flows with users since that provide advantageous data. The exploration of interaction consist in two step; the firs is data perception: when the system track the users feedbacks The the feedbacks management via machine learning techniques and basically three module Reinforcement, Evaluation and update module Reinforcement consists on a process of consolidation of decision basing on user behavior; a clever exploration of behavioral pattern is required to ensure an accurate reinforcement. Evaluation is more based on calculating the gap between user attempts and system recommendations in order to optimize the quality of approvals. Both evaluation and reinforcement exhibit the learning purposes. They trigger the updating module by injecting new rules or modifying existing ones, in term of effects and priorities, according to user preferences deduced during the interaction .
- The adaptation management layer is based on extraction and classification of rules and patterns Whish are the most popular ml techniques serving as a ‘Rule learners’ , based on supervised learning approaches, which is basically a rule management systems tools treating the users feedbacks and the set of existing adaptation rules to generate Learns new decision rules , which can be added to n the adaptation rule repository after being evaluated according to a promoting demoting techniques which give to the rule manager a new dimension of self improving learning capabilities.
- The adaptation management layer is based on extraction and classification of rules and patterns Whish are the most popular ml techniques serving as a ‘Rule learners’ , based on supervised learning approaches, which is basically a rule management systems tools treating the users feedbacks and the set of existing adaptation rules to generate Learns new decision rules , which can be added to n the adaptation rule repository after being evaluated according to a promoting demoting techniques which give to the rule manager a new dimension of self improving learning capabilities.
- The adaptation management layer is based on extraction and classification of rules and patterns Whish are the most popular ml techniques serving as a ‘Rule learners’ , based on supervised learning approaches, which is basically a rule management systems tools treating the users feedbacks and the set of existing adaptation rules to generate Learns new decision rules , which can be added to n the adaptation rule repository after being evaluated according to a promoting demoting techniques which give to the rule manager a new dimension of self improving learning capabilities.
- The second aims of this research is to avail adaptation based on learning for model based user interfaces , and basically in different levels of UI generation according to the CAMELEON reference framework ]. The focus of this point is to capitalize on learning based adaptation to improve the validity of the Abstract User Interface. Since , EVEN THAT Several algorithms were defined for the AUI definition however a lack of validity and consistency control still arise . Moreover, an intended reward of ML technique’s potential to be envisaged for the concrete user interfaces . Although some ML techniques were already explored , ML is consistently promising to emphasize new scenarios for the adaptation in the concrete UI level, for instance for the widget selection ML techniques seems great to manage all adaptation rules and guideline.
- The second aims of this research is to avail adaptation based on learning for model based user interfaces , and basically in different levels of UI generation according to the CAMELEON reference framework ]. The focus of this point is to capitalize on learning based adaptation to improve the validity of the Abstract User Interface. Since , EVEN THAT Several algorithms were defined for the AUI definition however a lack of validity and consistency control still arise . Moreover, an intended reward of ML technique’s potential to be envisaged for the concrete user interfaces . Although some ML techniques were already explored , ML is consistently promising to emphasize new scenarios for the adaptation in the concrete UI level, for instance for the widget selection ML techniques seems great to manage all adaptation rules and guideline.