CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Enhancing E-learning platforms with social networks mining

Social Networks appeared as an Internet application that offers several tools to create a personal virtual profile, add other users as friends, and interact with them through messages. These networks quickly evolved and won particular importance in people lives. Now, everyday, people use social networks to share news, interests, and discuss topics that in some way are important to them. Together with social networks, e-learning platforms and related technologies have evolved in the recent years. Both platforms and technologies (social networks and e-learning) enable access to specific information and are able to redirect specific content to an individual person. This dissertation is motivated on social networks data mining over e-learning platforms. It considers the following four social networks: Facebook, Twitter, Google Plus, and Delicious. In order to acquire, analyze, and make a correct and precise implementation of data, two different approaches were followed: enhancement of a current e-learning platform and improvement of search engines. The first approach proposes and elaborates a recommendation tool for Web documents using, as main criterion, social information to support a custom Learning Management System (LMS). In order to create the proposed system, three distinct applications (the Crawler, the SocialRank, and the Recommender) were proposed. Such data will be then incorporated into an LMS system, such as the Personal Learning Environment Box (PLEBOX). PLEBOX is a custom platform based on operating systems layout, and also, provides a software development kit (SDK), a group of tools, to create and manage modules. The results of recommendation tool about ten course units are presented. The second part presents an approach to improve a search engine based on social networks content. Subsequently, a depth analysis to justify the abovementioned procedures in order to create the SocialRank is presented. Finally, the results are presented and validated together with a custom search engine. Then, a solution to integrate and offer an order improvement of Web contents in a search engine was proposed, created, demonstrated, and validated, and it is ready for use.As redes sociais surgiram como um serviço Web com funcionalidades de criação de perfil, criação e interação de amigos. Estas redes evoluíram rapidamente e ganharam uma determinada importância na vida das pessoas. Agora, todos os dias, as pessoas usam as redes sociais para partilhar notícias, interesses e discutir temas que de alguma forma são importantes para elas. Juntamente com as redes sociais, as plataformas de aprendizagem baseadas em tecnologias, conhecidas como plataformas E-learning têm evoluído muito nos últimos anos. Ambas as plataformas e tecnologias (redes sociais e E-learning) fornecem acesso a informações específicas e são capazes de redirecionar determinado conteúdo para um ou vários indivíduos (personalização). O tema desta dissertação é motivado pela mineração do conteúdo das redes sociais em plataformas E-learning. Neste sentido, foram selecionadas quatro redes sociais, Facebook, Twitter, Google Plus, e Delicious para servir de estudo de caso à solução proposta. A fim de adquirir, analisar e concretizar uma aplicação correta e precisa dos dados, duas abordagens diferentes foram seguidas: enriquecimento de uma plataforma E-learning atual e melhoria dos motores de busca. A primeira abordagem propõe e elaboração de uma ferramenta de recomendação de documentos Web usando, como principal critério, a informação social para apoiar um sistema de gestão de aprendizagem (LMS). Desta forma, foram construídas três aplicações distintas, designadas por Crawler, SocialRank e Recommender. As informações extraídas serão incorporadas num sistema E-learning, tendo sido escolhida a PLEBOX (Personal Learning Environment Box). A PLEBOX é uma plataforma personalizada baseada numa interface inspirada nos sistemas operativos, fornecendo um conjunto de ferramentas (os conhecidos SDK - software development kit), para a criação e gestão de módulos. Dez unidades curriculares foram avaliadas e os resultados do sistema de recomendação são apresentados. A segunda abordagem apresenta uma proposta para melhorar um motor de busca com base no conteúdo das redes sociais. Subsequentemente, uma análise profunda é apresentada, justificando os procedimentos de avaliação, afim de criar o ranking de resultados (o SocialRank). Por último, os resultados são apresentados e validados em conjunto com um motor de busca. Assim, foi proposta, construída, demonstrada e avaliada uma solução para integrar e oferecer uma melhoria na ordenação de conteúdos Web dentro de um motor de busca. A solução está pronta para ser utilizad

A Framework for Personalized Content Recommendations to Support Informal Learning in Massively Diverse Information WIKIS

Personalization has proved to achieve better learning outcomes by adapting to specific learners’ needs, interests, and/or preferences. Traditionally, most personalized learning software systems focused on formal learning. However, learning personalization is not only desirable for formal learning, it is also required for informal learning, which is self-directed, does not follow a specified curriculum, and does not lead to formal qualifications. Wikis among other informal learning platforms are found to attract an increasing attention for informal learning, especially Wikipedia. The nature of wikis enables learners to freely navigate the learning environment and independently construct knowledge without being forced to follow a predefined learning path in accordance with the constructivist learning theory. Nevertheless, navigation on information wikis suffer from several limitations. To support informal learning on Wikipedia and similar environments, it is important to provide easy and fast access to relevant content. Recommendation systems (RSs) have long been used to effectively provide useful recommendations in different technology enhanced learning (TEL) contexts. However, the massive diversity of unstructured content as well as user base on such information oriented websites poses major challenges when designing recommendation models for similar environments. In addition to these challenges, evaluation of TEL recommender systems for informal learning is rather a challenging activity due to the inherent difficulty in measuring the impact of recommendations on informal learning with the absence of formal assessment and commonly used learning analytics. In this research, a personalized content recommendation framework (PCRF) for information wikis as well as an evaluation framework that can be used to evaluate the impact of personalized content recommendations on informal learning from wikis are proposed. The presented recommendation framework models learners’ interests by continuously extrapolating topical navigation graphs from learners’ free navigation and applying graph structural analysis algorithms to extract interesting topics for individual users. Then, it integrates learners’ interest models with fuzzy thesauri for personalized content recommendations. Our evaluation approach encompasses two main activities. First, the impact of personalized recommendations on informal learning is evaluated by assessing conceptual knowledge in users’ feedback. Second, web analytics data is analyzed to get an insight into users’ progress and focus throughout the test session. Our evaluation revealed that PCRF generates highly relevant recommendations that are adaptive to changes in user’s interest using the HARD model with rank-based mean average precision (MAP@k) scores ranging between 100% and 86.4%. In addition, evaluation of informal learning revealed that users who used Wikipedia with personalized support could achieve higher scores on conceptual knowledge assessment with average score of 14.9 compared to 10.0 for the students who used the encyclopedia without any recommendations. The analysis of web analytics data show that users who used Wikipedia with personalized recommendations visited larger number of relevant pages compared to the control group, 644 vs 226 respectively. In addition, they were also able to make use of a larger number of concepts and were able to make comparisons and state relations between concepts

Collaborative Summarization: When Collaborative Filtering Meets Document Summarization

PACLIC 23 / City University of Hong Kong / 3-5 December 200

Congenial Web Search : A Conceptual Framework for Personalized, Collaborative, and Social Peer-to-Peer Retrieval

Traditional information retrieval methods fail to address the fact that information consumption and production are social activities. Most Web search engines do not consider the social-cultural environment of users' information needs and the collaboration between users. This dissertation addresses a new search paradigm for Web information retrieval denoted as Congenial Web Search. It emphasizes personalization, collaboration, and socialization methods in order to improve effectiveness. The client-server architecture of Web search engines only allows the consumption of information. A peer-to-peer system architecture has been developed in this research to improve information seeking. Each user is involved in an interactive process to produce meta-information. Based on a personalization strategy on each peer, the user is supported to give explicit feedback for relevant documents. His information need is expressed by a query that is stored in a Peer Search Memory. On one hand, query-document associations are incorporated in a personalized ranking method for repeated information needs. The performance is shown in a known-item retrieval setting. On the other hand, explicit feedback of each user is useful to discover collaborative information needs. A new method for a controlled grouping of query terms, links, and users was developed to maintain Virtual Knowledge Communities. The quality of this grouping represents the effectiveness of grouped terms and links. Both strategies, personalization and collaboration, tackle the problem of a missing socialization among searchers. Finally, a concept for integrated information seeking was developed. This incorporates an integrated representation to improve effectiveness of information retrieval and information filtering. An integrated information retrieval process explores a virtual search network of Peer Search Memories in order to accomplish a reputation-based ranking. In addition, the community structure is considered by an integrated information filtering process. Both concepts have been evaluated and shown to have a better performance than traditional techniques. The methods presented in this dissertation offer the potential towards more transparency, and control of Web search

Mining Frequent Generalized Patterns for Web Personalization in the presence of Taxonomies

The Web is a continuously evolving environment, since its content is updated on a regular basis. As a result, the traditional usage-based approach to generate recommendations that takes as input the navigation paths recorded on the Web page level, is not as effective. Moreover, most of the content available online is either explicitly or implicitly characterized by a set of categories organized in a taxonomy, allowing the page-level navigation patterns to be generalized to a higher, aggregate level. In this direction, the authors present the Frequent Generalized Pattern (FGP) algorithm. FGP takes as input the transaction data and a hierarchy of categories and produces generalized association rules that contain transaction items and/or item categories. The results can be used to generate association rules and subsequently recommendations for the users. The algorithm can be applied to the log files of a typical Web site; however, it can be more helpful in a Web 2.0 application, such as a feed aggregator or a digital library mediator, where content is semantically annotated and the taxonomic nature is more complex, requiring us to extend FGP in a version called FGP+. The authors experimentally evaluate both algorithms using Web log data collected from a newspaper Web site

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports