Document Recommendation in Organizations with Personal Folders

In organizations, knowledge workers usually have their own personal folders that store and organize needed codified knowledge (textual documents) in taxonomy. In such personal folder environments, providing knowledge workers needed knowledge from other workers’ folders is important to facilitate knowledge sharing. This work adopts recommendation techniques to provide knowledge workers needed textual documents from other workers folders. Experiments are conducted to verify the performance of various methods using data collected from a research institute laboratory. The result shows that the CBF approach outperforms other methods

Comparing title only and full text indexing to classify web pages into bookmark categories

Web browser bookmark files are used to retain and organise records of web sites that the user would like to revisit. However, bookmark files tend to be under-utilised, as time and effort is needed to keep them organised. We use two methods to index and automatically classify documents referred to in 80 bookmark files, based on document title-only and full-text indexing, respectively. We evaluate the indexing methods by selecting a bookmark entry to classify from a bookmark file, and recreating the bookmark file so that it contains only entries created before the selected bookmark entry. Classification based on full-text indexing generally outperforms that based on document title only indexing. The ability to recommend the correct category at rank 1 using full-text indexing ranges from 20% to 41%, depending on the number of category members. However, combining the approaches results in a increase to 37% — 59%, but we would need to recommend up to two categories to users. By recommending up to 10 categories, this increases to 58% — 80%.peer-reviewe

Automatic Concept Extraction in Semantic Summarization Process

The Semantic Web offers a generic infrastructure for interchange, integration and creative reuse of structured data, which can help to cross some of the boundaries that Web 2.0 is facing. Currently, Web 2.0 offers poor query possibilities apart from searching by keywords or tags. There has been a great deal of interest in the development of semantic-based systems to facilitate knowledge representation and extraction and content integration [1], [2]. Semantic-based approach to retrieving relevant material can be useful to address issues like trying to determine the type or the quality of the information suggested from a personalized environment. In this context, standard keyword search has a very limited effectiveness. For example, it cannot filter for the type of information, the level of information or the quality of information. Potentially, one of the biggest application areas of content-based exploration might be personalized searching framework (e.g., [3],[4]). Whereas search engines provide nowadays largely anonymous information, new framework might highlight or recommend web pages related to key concepts. We can consider semantic information representation as an important step towards a wide efficient manipulation and retrieval of information [5], [6], [7]. In the digital library community a flat list of attribute/value pairs is often assumed to be available. In the Semantic Web community, annotations are often assumed to be an instance of an ontology. Through the ontologies the system will express key entities and relationships describing resources in a formal machine-processable representation. An ontology-based knowledge representation could be used for content analysis and object recognition, for reasoning processes and for enabling user-friendly and intelligent multimedia content search and retrieval. Text summarization has been an interesting and active research area since the 60’s. The definition and assumption are that a small portion or several keywords of the original long document can represent the whole informatively and/or indicatively. Reading or processing this shorter version of the document would save time and other resources [8]. This property is especially true and urgently needed at present due to the vast availability of information. Concept-based approach to represent dynamic and unstructured information can be useful to address issues like trying to determine the key concepts and to summarize the information exchanged within a personalized environment. In this context, a concept is represented with a Wikipedia article. With millions of articles and thousands of contributors, this online repository of knowledge is the largest and fastest growing encyclopedia in existence. The problem described above can then be divided into three steps: • Mapping of a series of terms with the most appropriate Wikipedia article (disambiguation). • Assigning a score for each item identified on the basis of its importance in the given context. • Extraction of n items with the highest score. Text summarization can be applied to many fields: from information retrieval to text mining processes and text display. Also in personalized searching framework text summarization could be very useful. The chapter is organized as follows: the next Section introduces personalized searching framework as one of the possible application areas of automatic concept extraction systems. Section three describes the summarization process, providing details on system architecture, used methodology and tools. Section four provides an overview about document summarization approaches that have been recently developed. Section five summarizes a number of real-world applications which might benefit from WSD. Section six introduces Wikipedia and WordNet as used in our project. Section seven describes the logical structure of the project, describing software components and databases. Finally, Section eight provides some consideration..

Dataset-driven research for improving recommender systems for learning

Verbert, K., Drachsler, H., Manouselis, N., Wolpers, M., Vuorikari, R., & Duval, E. (2011). Dataset-driven research for improving recommender systems for learning. In Ph. Long, & G. Siemens (Eds.), Proceedings of 1st International Conference Learning Analytics & Knowledge (pp. 44-53). February, 27-March, 1, 2011, Banff, Alberta, Canada. http://dl.acm.org/citation.cfm?id=2090122&CFID=77368864&CFTOKEN=72282583In the world of recommender systems, it is a common practice to use public available datasets from different application environments (e.g. MovieLens, Book-Crossing, or EachMovie) in order to evaluate recommendation algorithms. These datasets are used as benchmarks to develop new recommendation algorithms and to compare them to other algorithms in given settings. In this paper, we explore datasets that capture learner interactions with tools and resources. We use the datasets to evaluate and compare the performance of different recommendation algorithms for Technology Enhanced Learning (TEL). We present an experimental comparison of the accuracy of several collaborative filtering algorithms applied to these TEL datasets and elaborate on implicit relevance data, such as downloads and tags, that can be used to augment explicit relevance evidence in order to improve the performance of recommendation algorithms.dataTEL, STELLAR, AlterEgo, VOA3

Simulating light-weight Personalised Recommender Systems in Learning Networks: A case for Pedagogy-Oriented and Rating-based Hybrid Recommendation Strategies

Nadolski, R. J., Van den Berg, B., Berlanga, A. J., Drachsler, H., Hummel, H. G. K., Koper, R., & Sloep, P. B. (2009). Simulating Light-Weight Personalised Recommender Systems in Learning Networks: A Case for Pedagogy-Oriented and Rating-Based Hybrid Recommendation Strategies. Journal of Artificial Societies and Social Simulation 12(1)4 <http://jasss.soc.surrey.ac.uk/12/1/4.html>.Recommender systems for e-learning demand specific pedagogy-oriented and hybrid recommendation strategies. Current systems are often based on time-consuming, top down information provisioning combined with intensive data-mining collaborative filtering approaches. However, such systems do not seem appropriate for Learning Networks where distributed information can often not be identified beforehand. Sound way-finding for lifelong learners in Learning Networks requires dedicated personalised recommender systems (PRS) which should also be practically feasible with minimized effort. Currently, such light-weight PRS systems are scarcely available. This study shows that simulations can support defining PRS requirements prior to starting the costly process of development, implementation, testing, revision, and before conducting field experiments with real learners. This study confirms that providing recommendations leads towards more effective, more satisfied, and faster goal achievement. Furthermore, this simulation study reveals that a rating-based light-weight hybrid PRS-system is a good alternative for ontology-based recommendations, in particular for low-level goal achievement. Finally, it is found that rating-based light-weight hybrid PRS-systems enable more effective, more satisfied, and faster goal attainment than peer-based light-weight hybrid PRS-systems (incorporating collaborative techniques without rating)

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like e-commerce. Consequently, these particular requirements motivate the incorporation of specific goals and methods in the evaluation process for TEL recommender systems. In this article, the diverse evaluation methods that have been applied to evaluate TEL recommender systems are investigated. A total of 235 articles are selected from major conferences, workshops, journals, and books where relevant work have been published between 2000 and 2014. These articles are quantitatively analysed and classified according to the following criteria: type of evaluation methodology, subject of evaluation, and effects measured by the evaluation. Results from the survey suggest that there is a growing awareness in the research community of the necessity for more elaborate evaluations. At the same time, there is still substantial potential for further improvements. This survey highlights trends and discusses strengths and shortcomings of the evaluation of TEL recommender systems thus far, thereby aiming to stimulate researchers to contemplate novel evaluation approaches.Laboratorio de Investigación y Formación en Informática Avanzad

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like e-commerce. Consequently, these particular requirements motivate the incorporation of specific goals and methods in the evaluation process for TEL recommender systems. In this article, the diverse evaluation methods that have been applied to evaluate TEL recommender systems are investigated. A total of 235 articles are selected from major conferences, workshops, journals, and books where relevant work have been published between 2000 and 2014. These articles are quantitatively analysed and classified according to the following criteria: type of evaluation methodology, subject of evaluation, and effects measured by the evaluation. Results from the survey suggest that there is a growing awareness in the research community of the necessity for more elaborate evaluations. At the same time, there is still substantial potential for further improvements. This survey highlights trends and discusses strengths and shortcomings of the evaluation of TEL recommender systems thus far, thereby aiming to stimulate researchers to contemplate novel evaluation approaches.Laboratorio de Investigación y Formación en Informática Avanzad

Proceedings of the 3rd Workshop on Social Information Retrieval for Technology-Enhanced Learning

Learning and teaching resource are available on the Web - both in terms of digital learning content and people resources (e.g. other learners, experts, tutors). They can be used to facilitate teaching and learning tasks. The remaining challenge is to develop, deploy and evaluate Social information retrieval (SIR) methods, techniques and systems that provide learners and teachers with guidance in potentially overwhelming variety of choices. The aim of the SIRTEL’09 workshop is to look onward beyond recent achievements to discuss specific topics, emerging research issues, new trends and endeavors in SIR for TEL. The workshop will bring together researchers and practitioners to present, and more importantly, to discuss the current status of research in SIR and TEL and its implications for science and teaching

Entornos de aprendizaje móviles adaptativos y evaluación: CoMoLe y GeSES

This article presents the basis and experience with two systems that support the creation and evaluation of adaptive mobile learning environments. In this type of environment, dynamically generated by CoMoLE, the most suitable activities to be carried out by each student are recommended, so that s/he can benefit from spare time. The interface to support activity accomplishment is adapted by selecting the most suitable contents and tools for each student. To this end, student features, needs, previous interactions and context are considered. However, evaluating whether the recommendations and adaptation fit the student’s needs is complex. With the purpose of evaluating adaptive learning systems, the method GeSES was designed. GeSES uses Data Mining techniques to extract information about potential problems. It has been used to evaluate a CoMoLE-based learning  environment and the results obtained are presented in this article.En este artículo se presentan los fundamentos y experiencias de uso de dos sistemas que dan soporte a la creación y evaluación, respectivamente, de entornos de aprendizaje móviles adaptativos. En estos entornos, generados dinámicamente por el sistema CoMoLE, se recomiendan las actividades más adecuadas para ser realizadas por cada estudiante en cada momento, facilitándole así el aprovechamiento de su tiempo disponible; también se adapta la interfaz que da soporte a la realización de las actividades, seleccionando los contenidos y herramientas más apropiados en cada caso. Para ello, se consideran las características y necesidades del estudiante, sus acciones previas y el contexto en que se encuentra en ese momento. Sin embargo, es complejo evaluar cuán satisfactoriamente las recomendaciones y adaptaciones atienden las necesidades de cada usuario. Con el objetivo de evaluar entornos de enseñanza adaptativos, se diseñó el método GeSES que, utilizando técnicas de Minería de Datos, extrae, de los logs del sistema adaptativo, información sobre los puntos donde los estudiantes tuvieron mayores dificultades. Este método se ha utilizado para evaluar un entorno generado por CoMoLE. Los resultados obtenidos se presentan también en este artículo