REVIEW AND ANALYSIS OF MACHINE LEARNING AND SOFT COMPUTING APPROACHES FOR USER MODELING

ABSTRAC

Dynamic user profiles for web personalisation

Web personalisation systems are used to enhance the user experience by providing tailor-made services based on the user’s interests and preferences which are typically stored in user profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the users’ changing behaviour. In this paper, we introduce a set of methods designed to capture and track user interests and maintain dynamic user profiles within a personalisation system. User interests are represented as ontological concepts which are constructed by mapping web pages visited by a user to a reference ontology and are subsequently used to learn short-term and long-term interests. A multi-agent system facilitates and coordinates the capture, storage, management and adaptation of user interests. We propose a search system that utilises our dynamic user profile to provide a personalised search experience. We present a series of experiments that show how our system can effectively model a dynamic user profile and is capable of learning and adapting to different user browsing behaviours

Scalable Intelligence for Scheduling Systems

A personalização é um aspeto chave de uma interação homem-computador efetiva. Numa era em que existe uma abundância de informação e tantas pessoas a interagir com ela, de muitas maneiras, a capacidade de se ajustar aos seus utilizadores é crucial para qualquer sistema moderno. A criação de sistemas adaptáveis é um domínio bastante complexo que necessita de métodos muito específicos para ter sucesso. No entanto, nos dias de hoje ainda não existe um modelo ou arquitetura padrão para usar nos sistemas adaptativos modernos. A principal motivação desta tese é a proposta de uma arquitetura para modelação do utilizador que seja capaz de incorporar diferentes módulos necessários para criar um sistema com inteligência escalável com técnicas de modelação. Os módulos cooperam de forma a analisar os utilizadores e caracterizar o seu comportamento, usando essa informação para fornecer uma experiência de sistema customizada que irá aumentar não só a usabilidade do sistema mas também a produtividade e conhecimento do utilizador. A arquitetura proposta é constituída por três componentes: uma unidade de informação do utilizador, uma estrutura matemática capaz de classificar os utilizadores e a técnica a usar quando se adapta o conteúdo. A unidade de informação do utilizador é responsável por conhecer os vários tipos de indivíduos que podem usar o sistema, por capturar cada detalhe de interações relevantes entre si e os seus utilizadores e também contém a base de dados que guarda essa informação. A estrutura matemática é o classificador de utilizadores, e tem como tarefa a sua análise e classificação num de três perfis: iniciado, intermédio ou avançado. Tanto as redes de Bayes como as neuronais são utilizadas, e uma explicação de como as preparar e treinar para lidar com a informação do utilizador é apresentada. Com o perfil do utilizador definido torna-se necessária uma técnica para adaptar o conteúdo do sistema. Nesta proposta, uma abordagem de iniciativa mista é apresentada tendo como base a liberdade de tanto o utilizador como o sistema controlarem a comunicação entre si. A arquitetura proposta foi desenvolvida como parte integrante do projeto ADSyS - um sistema de escalonamento dinâmico - utilizado para resolver problemas de escalonamento sujeitos a eventos dinâmicos. Possui uma complexidade elevada mesmo para utilizadores frequentes, daí a necessidade de adaptar o seu conteúdo de forma a aumentar a sua usabilidade. Com o objetivo de avaliar as contribuições deste trabalho, um estudo computacional acerca do reconhecimento dos utilizadores foi desenvolvido, tendo por base duas sessões de avaliação de usabilidade com grupos de utilizadores distintos. Foi possível concluir acerca dos benefícios na utilização de técnicas de modelação do utilizador com a arquitetura proposta.Personalization is a key aspect of effective Human-Computer Interaction. The ability to adjust itself to its users is crucial to any modern system, in an era where there is so much information and so many people interacting in so many ways. The creation of adaptable systems is a complex domain that requires very specific methods in order to be successful. However, still today there is no standard model or architecture to use on a modern adaptive system. The main motivation of this dissertation is to propose an architecture for user modelling that is able to incorporate separate modules required to create a scalable intelligence system with user modelling techniques. The modules cooperate in order to analyse users and characterize their behaviour, using that information to provide a customized system experience that will increase not only the usability of the system but also the user’s productivity and knowledge. The proposed architecture is composed by three components: a user information unit, a mathematical structure able to classify users and the technique to use when adapting content. The user information unit is responsible for knowing the several types of individuals that can use the system, for capturing every part of relevant interaction between itself and its users and also contains the database which stores that information. The mathematical structure is the user classifier and is in charge of analysing the users and classifying them into one of three roles: beginner, intermediate or expert. Both Bayesian and Artificial Neural Networks are used, and an explanation on how to prepare and train them to deal with user information is provided. With the user role defined, a proper technique to adapt system’s content is required. In this work, a Mixed-Initiative approach is detailed which is based on allowing both the user and the system to gain control in the communication between them. The proposed architecture was developed as part of the ADSyS project. ADSyS is a Dynamic Scheduling system to solve scheduling problems subject to dynamic events. It has a high complexity even for frequent users, hence the need for the adaptation of its content to increase its usability. In order to evaluate the contribution of this work, a computational study of the user recognition was developed, as well as two usability evaluation sessions with distinct users. It was possible to conclude about the benefits of employing user modelling techniques with the proposed architecture

User modeling for exploratory search on the Social Web. Exploiting social bookmarking systems for user model extraction, evaluation and integration

Exploratory search is an information seeking strategy that extends be- yond the query-and-response paradigm of traditional Information Retrieval models. Users browse through information to discover novel content and to learn more about the newly discovered things. Social bookmarking systems integrate well with exploratory search, because they allow one to search, browse, and filter social bookmarks. Our contribution is an exploratory tag search engine that merges social bookmarking with exploratory search. For this purpose, we have applied collaborative filtering to recommend tags to users. User models are an im- portant prerequisite for recommender systems. We have produced a method to algorithmically extract user models from folksonomies, and an evaluation method to measure the viability of these user models for exploratory search. According to our evaluation web-scale user modeling, which integrates user models from various services across the Social Web, can improve exploratory search. Within this thesis we also provide a method for user model integra- tion. Our exploratory tag search engine implements the findings of our user model extraction, evaluation, and integration methods. It facilitates ex- ploratory search on social bookmarks from Delicious and Connotea and pub- lishes extracted user models as Linked Data

Improving accuracy of recommender systems through triadic closure

The exponential growth of social media services led to the information overload problem which information filtering and recommender systems deal by exploiting various techniques. One popular technique for making recommendations is based on trust statements between users in a social network. Yet explicit trust statements are usually very sparse leading to the need for expanding the trust networks by inferring new trust relationships. Existing methods exploit the propagation property of trust to expand the existing trust networks; however, their performance is strongly affected by the density of the trust network. Nevertheless, the utilisation of existing trust networks can model the users’ relationships, enabling the inference of new connections. The current study advances the existing methods and techniques on developing a trust-based recommender system proposing a novel method to infer trust relationships and to achieve a fully-expanded trust network. In other words, the current study proposes a novel, effective and efficient approach to deal with the information overload by expanding existing trust networks so as to increase accuracy in recommendation systems. More specifically, this study proposes a novel method to infer trust relationships, called TriadicClosure. The method is based on the homophily phenomenon of social networks and, more specifically, on the triadic closure mechanism, which is a fundamental mechanism of link formation in social networks via which communities emerge naturally, especially when the network is very sparse. Additionally, a method called JaccardCoefficient is proposed to calculate the trust weight of the inferred relationships based on the Jaccard Cofficient similarity measure. Both the proposed methods exploit structural information of the trust graph to infer and calculate the trust value. Experimental results on real-world datasets demonstrate that the TriadicClosure method outperforms the existing state-of-the-art methods by substantially improving prediction accuracy and coverage of recommendations. Moreover, the method improves the performance of the examined state-of-the-art methods in terms of accuracy and coverage when combined with them. On the other hand, the JaccardCoefficient method for calculating the weight of the inferred trust relationships did not produce stable results, with the majority showing negative impact on the performance, for both accuracy and coverage