Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

Exploiting the conceptual space in hybrid recommender systems: a semantic-based approach

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 200

Automatic news recommendations via aggregated profiling

Today, people have only limited, valuable leisure time at their hands which they want to fill in as good as possible according to their own interests, whereas broadcasters want to produce and distribute news items as fast and targeted as possible. These (developing) news stories can be characterised as dynamic, chained, and distributed events in addition to which it is important to aggregate, link, enrich, recommend, and distribute these news event items as targeted as possible to the individual, interested user. In this paper, we show how personalised recommendation and distribution of news events, described using an RDF/OWL representation of the NewsML-G2 standard, can be enabled by automatically categorising and enriching news events metadata via smart indexing and linked open datasets available on the web of data. The recommendations-based on a global, aggregated profile, which also takes into account the (dis)likings of peer friends-are finally fed to the user via a personalised RSS feed. As such, the ultimate goal is to provide an open, user-friendly recommendation platform that harnesses the end-user with a tool to access useful news event information that goes beyond basic information retrieval. At the same time, we provide the (inter)national community with standardised mechanisms to describe/distribute news event and profile information

Review of Current Student-Monitoring Techniques used in eLearning-Focused recommender Systems and Learning analytics. The Experience API & LIME model Case Study

Recommender systems require input information in order to properly operate and deliver content or behaviour suggestions to end users. eLearning scenarios are no exception. Users are current students and recommendations can be built upon paths (both formal and informal), relationships, behaviours, friends, followers, actions, grades, tutor interaction, etc. A recommender system must somehow retrieve, categorize and work with all these details. There are several ways to do so: from raw and inelegant database access to more curated web APIs or even via HTML scrapping. New server-centric user-action logging and monitoring standard technologies have been presented in past years by several groups, organizations and standard bodies. The Experience API (xAPI), detailed in this article, is one of these. In the first part of this paper we analyse current learner-monitoring techniques as an initialization phase for eLearning recommender systems. We next review standardization efforts in this area; finally, we focus on xAPI and the potential interaction with the LIME model, which will be also summarized below

Hybrid approach to content recommendation

Tese de Mestrado Integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

Traceability-based access recommendation

Devido à grande quantidade de dados disponíveis na Internet, um dos maiores desafios no mundo virtual é recomendar informação aos seus utilizadores. Por outro lado, esta grande quantidade de dados pode ser útil para melhorar recomendações se for anotada e interligada por dados de proveniência. Neste trabalho é abordada a temática de recomendação de (alteração de) permissões acesso sobre recursos ao seu proprietário, ao invés da recomendação do próprio recurso a um potencial consumidor/leitor. Para permitir a recomendação de acessos a um determinado recurso, independentemente do domínio onde o mesmo se encontra alojado, é essencial a utilização de sistemas de controlo de acessos distribuídos, mecanismos de rastreamento de recursos e recomendação independentes do domínio. Assim sendo, o principal objectivo desta tese é utilizar informação de rastreamento de acções realizadas sobre recursos (i.e. informação que relaciona recursos e utilizadores através da Web independentemente do domínio de rede) e utiliza-la para permitir a recomendação de privilégios de acesso a esses recursos por outros utilizadores. Ao longo do desenvolvimento da tese resultaram as seguintes contribuições: A análise do estado da arte de recomendação e de sistemas de recomendação potencialmente utilizáveis na recomendação de privilégios (secção 2.3); A análise do estado da arte de mecanismos de rastreamento e proveniência de informação (secção 2.2); A proposta de um sistema de recomendação de privilégios de acesso independente do domínio e a sua integração no sistema de controlo de acessos proposto anteriormente (secção 3.1); Levantamento, análise e especificação da informação relativa a privilégios de acesso, para ser utilizada no sistema de recomendação (secção 2.1); A especificação da informação resultante do rastreamento de acções para ser utilizada na recomendação de privilégios de acesso (secção 4.1.1); A especificação da informação de feedback resultante do sistema de recomendação de acessos e sua reutilização no sistema de recomendação(secção 4.1.3); A especificação, implementação e integração do sistema de recomendação de privilégios de acesso na plataforma já existente (secção 4.2 e secção 4.3); Realização de experiências de avaliação ao sistema de recomendação de privilégios, bem como a análise dos resultados obtidos (secção 5).Due to the large amount of available data in the internet, one of the biggest challenges in the virtual world is to recommend information to the user. On the other hand this large amount of data can be useful to improve recommendations if it is semantically described and inter-related. To describe and relate this information, provenance information is fundamental. Several resources are not totally recommendable but can be recommended a speci c type of access to them. So the cross-domain information provenance, cross-domain access control and cross-domain access recommendation are leading keys to improve cross-domain recommendation. The main goal of this thesis work is to use automatic traceability information of actions that are performed over resources in order to relate users and resources over the Web without relying on the domain and use this information to recommend access privileges to other users

An architecture for user preference-based IoT service selection in cloud computing using mobile devices for smart campus

The Internet of things refers to the set of objects that have identities and virtual personalities operating in smart spaces using intelligent interfaces to connect and communicate within social environments and user context. Interconnected devices communicating to each other or to other machines on the network have increased the number of services. The concepts of discovery, brokerage, selection and reliability are important in dynamic environments. These concepts have emerged as an important field distinguished from conventional distributed computing by its focus on large-scale resource sharing, delivery and innovative applications. The usage of Internet of Things technology across different service provisioning environments has increased the challenges associated with service selection and discovery. Although a set of terms can be used to express requirements for the desired service, a more detailed and specific user interface would make it easy for the users to express their requirements using high-level constructs. In order to address the challenge of service selection and discovery, we developed an architecture that enables a representation of user preferences and manipulates relevant descriptions of available services. To ensure that the key components of the architecture work, algorithms (content-based and collaborative filtering) derived from the architecture were proposed. The architecture was tested by selecting services using content-based as well as collaborative algorithms. The performances of the algorithms were evaluated using response time. Their effectiveness was evaluated using recall and precision. The results showed that the content-based recommender system is more effective than the collaborative filtering recommender system. Furthermore, the results showed that the content-based technique is more time-efficient than the collaborative filtering technique

Content Recommendation Through Linked Data

Nowadays, people can easily obtain a huge amount of information from the Web, but often they have no criteria to discern it. This issue is known as information overload. Recommender systems are software tools to suggest interesting items to users and can help them to deal with a vast amount of information. Linked Data is a set of best practices to publish data on the Web, and it is the basis of the Web of Data, an interconnected global dataspace. This thesis discusses how to discover information useful for the user from the vast amount of structured data, and notably Linked Data available on the Web. The work addresses this issue by considering three research questions: how to exploit existing relationships between resources published on the Web to provide recommendations to users; how to represent the user and his context to generate better recommendations for the current situation; and how to effectively visualize the recommended resources and their relationships. To address the first question, the thesis proposes a new algorithm based on Linked Data which exploits existing relationships between resources to recommend related resources. The algorithm was integrated into a framework to deploy and evaluate Linked Data based recommendation algorithms. In fact, a related problem is how to compare them and how to evaluate their performance when applied to a given dataset. The user evaluation showed that our algorithm improves the rate of new recommendations, while maintaining a satisfying prediction accuracy. To represent the user and their context, this thesis presents the Recommender System Context ontology, which is exploited in a new context-aware approach that can be used with existing recommendation algorithms. The evaluation showed that this method can significantly improve the prediction accuracy. As regards the problem of effectively visualizing the recommended resources and their relationships, this thesis proposes a visualization framework for DBpedia (the Linked Data version of Wikipedia) and mobile devices, which is designed to be extended to other datasets. In summary, this thesis shows how it is possible to exploit structured data available on the Web to recommend useful resources to users. Linked Data were successfully exploited in recommender systems. Various proposed approaches were implemented and applied to use cases of Telecom Italia