A COLLABORATIVE FILTERING APPROACH TO PREDICT WEB PAGES OF INTEREST FROMNAVIGATION PATTERNS OF PAST USERS WITHIN AN ACADEMIC WEBSITE

This dissertation is a simulation study of factors and techniques involved in designing hyperlink recommender systems that recommend to users, web pages that past users with similar navigation behaviors found interesting. The methodology involves identification of pertinent factors or techniques, and for each one, addresses the following questions: (a) room for improvement; (b) better approach, if any; and (c) performance characteristics of the technique in environments that hyperlink recommender systems operate in. The following four problems are addressed:Web Page Classification. A new metric (PageRank × Inverse Links-to-Word count ratio) is proposed for classifying web pages as content or navigation, to help in the discovery of user navigation behaviors from web user access logs. Results of a small user study suggest that this metric leads to desirable results.Data Mining. A new apriori algorithm for mining association rules from large databases is proposed. The new algorithm addresses the problem of scaling of the classical apriori algorithm by eliminating an expensive joinstep, and applying the apriori property to every row of the database. In this study, association rules show the correlation relationships between user navigation behaviors and web pages they find interesting. The new algorithm has better space complexity than the classical one, and better time efficiency under some conditionsand comparable time efficiency under other conditions.Prediction Models for User Interests. We demonstrate that association rules that show the correlation relationships between user navigation patterns and web pages they find interesting can be transformed intocollaborative filtering data. We investigate collaborative filtering prediction models based on two approaches for computing prediction scores: using simple averages and weighted averages. Our findings suggest that theweighted averages scheme more accurately computes predictions of user interests than the simple averages scheme does.Clustering. Clustering techniques are frequently applied in the design of personalization systems. We studied the performance of the CLARANS clustering algorithm in high dimensional space in relation to the PAM and CLARA clustering algorithms. While CLARA had the best time performance, CLARANS resulted in clusterswith the lowest intra-cluster dissimilarities, and so was most effective in this regard

Incremental mining techniques

Dissertação de mestrado em Sistemas de Dados e Processamento Analítico.The increasing necessity of organizational data exploration and analysis, seeking new knowledge that may be implicit in their operational systems, has made the study of data mining techniques gain a huge impulse. This impulse can be clearly noticed in the e-commerce domain, where the analysis of client’s past behaviours is extremely valuable and may, eventually, bring up important working instruments for determining his future behaviour. Therefore, it is possible to predict what a Web site visitor might be looking for, and thus restructuring the Web site to meet his needs. Thereby, the visitor keeps longer navigating in the Web site, what increases his probability of getting attracted by some product, leading to its purchase. To achieve this goal, Web site adaptation has to be fast enough to change while the visitor navigates, and has also to ensure that this adaptation is made according to the most recent visitors’ navigation behaviour patterns, which requires a mining algorithm with a sufficiently good response time for frequently update the patterns. Typical databases are continuously changing over the time, what can invalidate some patterns or introduce new ones. Thus, conventional data mining techniques were proved to be inefficient, as they needed to re-execute to update the mining results with the ones derived from the last database changes. Incremental mining techniques emerged to avoid algorithm re-execution and to update mining results when incremental data are added or old data are removed, ensuring a better performance in the data mining processes. In this work, we analyze some existing incremental mining strategies and models, giving a particular emphasis in their application on Web sites, in order to develop models to discover Web user behaviour patterns and automatically generate some recommendations to restructure sites in useful time. For accomplishing this task, we designed and implemented Spottrigger, a system responsible for the whole data life cycle in a Web site restructuring work. This life cycle includes tasks specially oriented to extract the raw data stored in Web servers, pass these data by intermediate phases of cleansing and preparation, perform an incremental data mining technique to extract users’ navigation patterns and finally suggesting new locations of spots on the Web site according to the patterns found and the profile of the visitor. We applied Spottrigger in our case study, which was based on data gathered from a real online newspaper. Our main goal was to collect, in a useful time, information about users that at a given moment are consulting the site and thus restructuring the Web site in a short term, delivering the scheduled advertisements, activated according to the user’s profile. Basically, our idea is to have advertisements classified in levels and restructure the Web site to have the higher level advertisements in pages the visitor will most probably access. In order to do that, we construct a page ranking for the visitor, based on results obtained through the incremental mining technique. Since visitors’ navigation behaviour may change during time, the incremental mining algorithm will be responsible for catching this behaviour changes and fast update the patterns. Using Spottrigger as a decision support system for advertisement, a newspaper company may significantly improve the merchandising of its publicity spots guaranteeing that a given advertisement will reach to a higher number of visitors, even if they change their behaviour when visiting pages that were usually not visited.A crescente necessidade de exploração e análise dos dados, na procura de novo conhecimento sobre o negócio de uma organização nos seus sistemas operacionais, tem feito o estudo das técnicas de mineração de dados ganhar um grande impulso. Este pode ser notado claramente no domínio do comércio electrónico, no qual a análise do comportamento passado dos clientes é extremamente valiosa e pode, eventualmente, fazer emergir novos elementos de trabalho, bastante válidos, para a determinação do seu comportamento no futuro. Desta forma, é possível prever aquilo que um visitante de um sítio Web pode andar à procura e, então, preparar esse sítio para atender melhor as suas necessidades. Desta forma, consegue-se fazer com que o visitante permaneça mais tempo a navegar por esse sítio o que aumenta naturalmente a possibilidade dele ser atraído por novos produtos e proceder, eventualmente, à sua aquisição. Para que este objectivo possa ser alcançado, a adaptação do sítio tem de ser suficientemente rápida para que possa acompanhar a navegação do visitante, ao mesmo tempo que assegura os mais recentes padrões de comportamento de navegação dos visitantes. Isto requer um algoritmo de mineração de dados com um nível de desempenho suficientemente bom para que se possa actualizar os padrões frequentemente. Com as constantes mudanças que ocorrem ao longo do tempo nas bases de dados, invalidando ou introduzindo novos padrões, as técnicas de mineração de dados convencionais provaram ser ineficientes, uma vez que necessitam de ser reexecutadas a fim de actualizar os resultados do processo de mineração com os dados subjacentes às modificações ocorridas na base de dados. As técnicas de mineração incremental surgiram com o intuito de evitar essa reexecução do algoritmo para actualizar os resultados da mineração quando novos dados (incrementais) são adicionados ou dados antigos são removidos. Assim, consegue-se assegurar uma maior eficiência aos processos de mineração de dados. Neste trabalho, analisamos algumas das diferentes estratégias e modelos para a mineração incremental de dados, dando-se particular ênfase à sua aplicação em sítios Web, visando desenvolver modelos para a descoberta de padrões de comportamento dos visitantes desses sítios e gerar automaticamente recomendações para a sua reestruturação em tempo útil. Para atingir esse objectivo projectámos e implementámos o sistema Spottrigger, que cobre todo o ciclo de vida do processo de reestruturação de um sítio Web. Este ciclo é composto, basicamente, por tarefas especialmente orientadas para a extracção de dados “crus” armazenados nos servidores Web, passar estes dados por fases intermédias de limpeza e preparação, executar uma técnica de mineração incremental para extrair padrões de navegação dos utilizadores e, finalmente, reestruturar o sítio Web de acordo com os padrões de navegação encontrados e com o perfil do próprio utilizador. Além disso, o sistema Spottrigger foi aplicado no nosso estudo de caso, o qual é baseado em dados reais provenientes de um jornal online. Nosso principal objectivo foi colectar, em tempo útil, alguma informação sobre o perfil dos utilizadores que num dado momento estão a consultar o sítio e, assim, fazer a reestruturação do sítio num período de tempo tão curto quanto o possível, exibindo os anúncios desejáveis, activados de acordo com o perfil do utilizador. Os anúncios do sistema estão classificados por níveis. Os sítios são reestruturados para que os anúncios de nível mais elevado sejam lançados nas páginas com maior probabilidade de serem visitadas. Nesse sentido, foi definida uma classificação das páginas para o utilizador, baseada nos padrões frequentes adquiridos através do processo de mineração incremental. Visto que o comportamento de navegação dos visitantes pode mudar ao longo do tempo, o algoritmo de mineração incremental será também responsável por capturar essas mudanças de comportamento e rapidamente actualizar os padrões.

Modelling Web Usage in a Changing Environment

Eiben, A.E. [Promotor]Kowalczyk, W. [Copromotor

Multi modal multi-semantic image retrieval

PhDThe rapid growth in the volume of visual information, e.g. image, and video can overwhelm users’ ability to find and access the specific visual information of interest to them. In recent years, ontology knowledge-based (KB) image information retrieval techniques have been adopted into in order to attempt to extract knowledge from these images, enhancing the retrieval performance. A KB framework is presented to promote semi-automatic annotation and semantic image retrieval using multimodal cues (visual features and text captions). In addition, a hierarchical structure for the KB allows metadata to be shared that supports multi-semantics (polysemy) for concepts. The framework builds up an effective knowledge base pertaining to a domain specific image collection, e.g. sports, and is able to disambiguate and assign high level semantics to ‘unannotated’ images. Local feature analysis of visual content, namely using Scale Invariant Feature Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’ model (BVW) as an effective method to represent visual content information and to enhance its classification and retrieval. Local features are more useful than global features, e.g. colour, shape or texture, as they are invariant to image scale, orientation and camera angle. An innovative approach is proposed for the representation, annotation and retrieval of visual content using a hybrid technique based upon the use of an unstructured visual word and upon a (structured) hierarchical ontology KB model. The structural model facilitates the disambiguation of unstructured visual words and a more effective classification of visual content, compared to a vector space model, through exploiting local conceptual structures and their relationships. The key contributions of this framework in using local features for image representation include: first, a method to generate visual words using the semantic local adaptive clustering (SLAC) algorithm which takes term weight and spatial locations of keypoints into account. Consequently, the semantic information is preserved. Second a technique is used to detect the domain specific ‘non-informative visual words’ which are ineffective at representing the content of visual data and degrade its categorisation ability. Third, a method to combine an ontology model with xi a visual word model to resolve synonym (visual heterogeneity) and polysemy problems, is proposed. The experimental results show that this approach can discover semantically meaningful visual content descriptions and recognise specific events, e.g., sports events, depicted in images efficiently. Since discovering the semantics of an image is an extremely challenging problem, one promising approach to enhance visual content interpretation is to use any associated textual information that accompanies an image, as a cue to predict the meaning of an image, by transforming this textual information into a structured annotation for an image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct types of information representation and modality, there are some strong, invariant, implicit, connections between images and any accompanying text information. Semantic analysis of image captions can be used by image retrieval systems to retrieve selected images more precisely. To do this, a Natural Language Processing (NLP) is exploited firstly in order to extract concepts from image captions. Next, an ontology-based knowledge model is deployed in order to resolve natural language ambiguities. To deal with the accompanying text information, two methods to extract knowledge from textual information have been proposed. First, metadata can be extracted automatically from text captions and restructured with respect to a semantic model. Second, the use of LSI in relation to a domain-specific ontology-based knowledge model enables the combined framework to tolerate ambiguities and variations (incompleteness) of metadata. The use of the ontology-based knowledge model allows the system to find indirectly relevant concepts in image captions and thus leverage these to represent the semantics of images at a higher level. Experimental results show that the proposed framework significantly enhances image retrieval and leads to narrowing of the semantic gap between lower level machinederived and higher level human-understandable conceptualisation

Analytical study and computational modeling of statistical methods for data mining

Today, there is tremendous increase of the information available on electronic form. Day by day it is increasing massively. There are enough opportunities for research to retrieve knowledge from the data available in this information. Data mining and app

A Multi-Modal Incompleteness Ontology model (MMIO) to enhance 4 information fusion for image retrieval

This research has been supported in part by National Science and Technology Development (NSTDA), Thailand. Project No: SCH-NR2011-851