Automatic cLasification d a PArallelism Data Mining based User Modeling Systems for Web Personalization applied to people with disabilities J. Abascal, O. Arbelaitz, J. Muguerza, I. Perona Konputagailu Arkitektura eta Teknologia Saila Departamento de Arquitectura y Tecnología de Computadores

Contribution of Data Mining Schedule Introduction Contribution of Data Mining Profile generation Exploitation Summary Konputagailu Arkitektura eta Teknologia Saila Departamento de Arquitectura y Tecnología de Computadores

Objective in any context: Introduction Objective in any context: To adapt web pages to the need of the users Adaptation becomes especially critical when the users have special needs Blind people: needs sounds (speech synthesis)‏ Low vision: large images and font sizes, bright colours … Motor problems: simple links or buttons Cognitive disabilities: simpler texts We need to: Build a model of the user that aggregates its main characteristics Use this model to perform actions that make easier its information acquisition in the Web Konputagailu Arkitektura eta Teknologia Saila Departamento de Arquitectura y Tecnología de Computadores

How would we like to do this adaptation? Introduction This way the adaptive system can adapt the interaction to the concrete users How would we like to do this adaptation? Without the user doing an explicit demand Automatically and dynamically adapting to the general characteristics of the user and the moment situation (mood, physical state, used device…) Different kinds of features can be modeled to adjust the interface User Navigational behavior Preferences Physical sensory or cognitive restrictions Context Moment interest Device Konputagailu Arkitektura eta Teknologia Saila Departamento de Arquitectura y Tecnología de Computadores

Adaptive systems composed of Introduction Adaptive systems composed of Modeling component profiles and stereotypes to make assumptions about the characteristics of the user Usually built by means of ontologies (concept hierarchies)that allow to store, manipulate and extract assumptions from data about the user, its context, tasks, etc. Konputagailu Arkitektura eta Teknologia Saila Departamento de Arquitectura y Tecnología de Computadores

Two options to acquire information: General schema Ontology User Profiles Adapted interface User’s information Two options to acquire information: Manually Automatically Konputagailu Arkitektura eta Teknologia Saila Departamento de Arquitectura y Tecnología de Computadores

Directly designed by experts in the area. Rule based approach Manual option Directly designed by experts in the area. Rule based approach Rules generated based on the experts’ knowledge of the needs of different kinds of users Drawbacks: Profiles are artificially generated Need of the expert each time the interface needs to be adapted to a new kind of user Konputagailu Arkitektura eta Teknologia Saila Departamento de Arquitectura y Tecnología de Computadores

Automatic option When the user is a person with disabilities, data mining is a way to automatically process information about the uses of the person Data mining for web personalization is based on statistical data obtained from real navigation data When the characteristics of the user change, collected data allows the automatic change of the interaction schema Learning from the own interaction allows maintaining a dynamic profile of the user, avoiding the application of all assumptions when the interest, characteristics or circumstances of the user change Konputagailu Arkitektura eta Teknologia Saila Departamento de Arquitectura y Tecnología de Computadores

Different approaches to build models Automatic option Different approaches to build models Based on previous information from that user such as the logs of previous navigations (content-based approach) Information about groups of users with similar characteristics (collaborative approach) Our aim: to combine them to find a trade-off high specialization/computationally too expensive Obtaining user information in automatic approaches Two main sources: Obtained in the client part: desktop applications… Obtained in the server: browsing history… Using some of them can violate the user’s privacy Konputagailu Arkitektura eta Teknologia Saila Departamento de Arquitectura y Tecnología de Computadores

Techniques: unsupervised learning techniques or clustering techniques Automatic option Techniques: unsupervised learning techniques or clustering techniques Output: sets of users with similar characteristics or needs Need of a distance metric In a determined dimensional space: Manhattan, Euclidean distance, cosine similarity … For sequences (click streams in a web, visited web pages, etc.) Edit distance Normalized Compression Distance… The used clustering techniques (SAHN, Fixed-width or Leader algorithm, k-means) will depend on: The nature of the data (vector like, sequence like) The selected distances Konputagailu Arkitektura eta Teknologia Saila Departamento de Arquitectura y Tecnología de Computadores

Automatic option To establish the profiles related to each cluster or group of similar users we propose: Meta-learning techniques based for example in classification trees to profile the built clusters Paradigms such as association rules or frequent episodes so that we can predict the most probable transitions between links for a kind of user Konputagailu Arkitektura eta Teknologia Saila Departamento de Arquitectura y Tecnología de Computadores

Automatic option Advantages: The learners can automatically generate profiles for new kinds of users Profiles are generated based in real browsing data (not artificial) New profiles can appear if the users change (incremental learning, enrich the ontology) Profiles based on the concepts in the ontology (different combinations) Profiles with new concepts. Machine learning used to validate new concepts Need of an significance analysis Konputagailu Arkitektura eta Teknologia Saila Departamento de Arquitectura y Tecnología de Computadores

Exploitation . A user navigating in the web needs to be matched with one of the previously generated profiles or a new one Ontology User Profiles Adapted interface ?? Konputagailu Arkitektura eta Teknologia Saila Departamento de Arquitectura y Tecnología de Computadores

Two main approaches to decide the profile of a user Exploitation Two main approaches to decide the profile of a user Explicit: using some questionnaire (game??) Disturbs the user with an explicit information demand making him/ her feel uncomfortable. There is high probability of the user answering with not the whole true about his/her characteristics Will not adapt to the conditions of a user in an concrete moment (tired or not, in a hurry…) just adapted to one of the existing profiles Konputagailu Arkitektura eta Teknologia Saila Departamento de Arquitectura y Tecnología de Computadores

Implicit: based on the browsing information of the user Exploitation Implicit: based on the browsing information of the user This approach has the problem of the ti (cold start) The user does not feel uncomfortable There is no lying possibility Using machine learning can adapt to the conditions of a user in an concrete moment (tired or not, in a hurry…) Techniques: supervised classification based techniques Konputagailu Arkitektura eta Teknologia Saila Departamento de Arquitectura y Tecnología de Computadores

Data Mining techniques: Summary Goals: Acquisition of user information to build profiles Classification of new users on previously defined profiles or stereotypes or a new one Data Mining techniques: Unsupervised techniques combined with classification trees or similar Supervised techniques Data Mining contributions: From the user point of view: comfortable Incremental learning capacity. Adapting to new kinds of users of new situations Enrichment of ontologies (hybrid solutions) Konputagailu Arkitektura eta Teknologia Saila Departamento de Arquitectura y Tecnología de Computadores

Thanks for your attention!!! http://www.sc.ehu.es/aldapa/ olatz.arbelaitz@ehu.es Konputagailu Arkitektura eta Teknologia Saila Departamento de Arquitectura y Tecnología de Computadores