Continual Learning System with Sentence Embeddings

Recent improvements in computing power and ambient technologies have opened up new perspectives in ambient intelligence and proactive software systems. The need emerges for an ambient and supportive system that deeply understands the user\u27s needs and preferences under the current context. In this work, we propose a recommendation system that aggregates and understands the current situation and continuously learns from the user’s decision history. To leverage a rich pool of contextual data, the system dynamically changes the strategy and scope for contextualization and encodes multimodal data into unified embeddings. A contextual similarity database consisting of these embeddings is leveraged for finding, ranking and comparing scenarios. The proposed recommender is intended to fit into a decentralized service system and addresses challenges in application interaction, user engagement, user privacy and scalability

A Novel Personalized Academic Knowledge Sharing System in Online Social Network

Information overload is a major problem for both readers and authors due to the rapid increase in scientific papers in recent years. Methods are proposed to help readers find right papers, but few research focuses on knowledge sharing and dissemination from authors’ perspectives. This paper proposes a personalized academic knowledge sharing system that takes advantages of author’s initiatives. In our method, we combine the user-level and document-level analysis in the same model, it works in two stages: 1) user-level analysis, which is used to profile users in three dimensions (i.e., research topic relevance, social relation and research quality); and 2) document-level analysis, which calculates the similarity between the target article and reader’s publications. The proposed method has been implemented in the ScholarMate, which is a popular academic social network. The experiment results show that the proposed method can effectively promote the academic knowledge sharing, it outperforms other baseline methods

A Domain-Adaptable Heterogeneous Information Integration Platform: Tourism and Biomedicine Domains.

In recent years, information integration systems have become very popular in mashup-type applications. Information sources are normally presented in an individual and unrelated fashion, and the development of new technologies to reduce the negative effects of information dispersion is needed. A major challenge is the integration and implementation of processing pipelines using different technologies promoting the emergence of advanced architectures capable of processing such a number of diverse sources. This paper describes a semantic domain-adaptable platform to integrate those sources and provide high-level functionalities, such as recommendations, shallow and deep natural language processing, text enrichment, and ontology standardization. Our proposed intelligent domain-adaptable platform (IDAP) has been implemented and tested in the tourism and biomedicine domains to demonstrate the adaptability, flexibility, modularity, and utility of the platform. Questionnaires, performance metrics, and A/B control groups’ evaluations have shown improvements when using IDAP in learning environmentspost-print2139 K

Supply chain knowledge management: A linked data-based approach using SKOS

Nowadays, knowledge is a powerful tool in order to obtain benefits within organizations. This is especially true when semantic web technologies are being adapted for the requirements of enterprises. In this regard, the Simple Knowledge Organization System (SKOS) is an area of work developing specifications and standards to support the use of knowledge organization systems. Over recent years, SKOS has become one of the sweet spots in the linked data (LD) ecosystems. In this paper, we propose a linked data-based approach using SKOS, in order to manage the knowledge from supply chains. Additionally, this paper covers how SKOS can be enriched by ontologies and LD to further improve semantic information management. This is due to the fact that the supply chain literature focuses on assets, data, and information elements of exchange between supply chain partners, despite improved integration and collaboration requiring the development of more complex features of know-how and knowledge

What are the viewers’ reviews and emotions in Filmaffinity? A netnographic analysis

The increasing importance of online user feedback is also influencing the film industry business. This work analyses the perceptions of film audience in Spain major using netnography. A total of 2187 opinions collected in a specialized film forum have been analyzed through factorial analysis of simple correspondences. The two dimensions obtained, together with the other results, show the importance of intrinsic variables. The first dimension allows to identify the personal action against the so-called environment. The second dimension contrasts positive perceptions against negative ones, allowing to detect market opportunities associated with a good soundtrack and a good plot. This second axis will also facilitate the detection of the worst rated films

Um sistema de recomendação de conteúdo suportado pela computação distribuída

TCC (graduação) - Universidade Federal de Santa Catarina. Campus Araranguá. Curso de Tecnologias da Informação e Comunicação.Desde a sua criação, a Internet e mais especificamente a Web, vem passando por grandes modificações. Atualmente, usuários possuem um papel fundamental, não somente consumindo informações, mas também provendo novos conteúdos. Este cenário e os avanços da Tecnologia da Informação tem promovido um aumento vertiginoso no volume de informações disponíveis. A partir disto surgem desafios, entre eles, como permitir que o usuário realize escolhas mais adequadas. Neste contexto, encontram-se os Sistemas de Recomendação com o intuito de auxiliar usuários na tomada de decisão, bem como, a Computação Distribuída como infraestrutura de base para lidar com grandes volumes de informação. A partir disto, o presente trabalho propõe um sistema voltado à recomendação de conteúdo textual através das abordagens de filtragem colaborativa e baseada em conteúdo. Visando permitir a avaliação da proposição deste trabalho foi elaborado um modelo de dados e desenvolvido um protótipo. O protótipo possibilita a geração de informações nas duas principais abordagens de recomendação. Possui ainda a capacidade de realizar o processamento de maneira distribuída. As informações processadas e geradas através da aplicação do protótipo permitem a sugestão de itens, em que no presente trabalho se referem a documentos. Pode-se afirmar que os resultados no que tange a sugestão de conteúdo são consistentes e compatíveis com a literatura da área de Sistemas de Recomendação. Ressalta-se ainda que o desenvolvimento de sistemas distribuídos contribui para área em questão visto que o desempenho frente a grande volumes de informação é fundamental para que se possa produzir insumos que auxiliem usuários em suas escolhas.Since its creation the Internet and more specifically the Web has changed dramatically. Nowadays, users have a key role not only consuming information but also providing new content. This scenario and the advances in Information Technology have fostered the increase in the volume of information available. From this challenges arise, among them, how to allow users to perform more appropriate choices. In this context, there are the Recommender Systems in order to aid users in decision making and Distributed Computing as the base infrastructure to handle large volumes of information. From this, the present work proposes a system towards recommendation of textual content through collaborative filtering and content-based approaches. To allow the evaluation of the proposition a data model has been designed as well as has been developed a prototype. The prototype enables the generation of information on the two major recommendation approaches. It also has the ability to carry out the processing in a distributed manner. The information generated and processed by the prototype allows the suggestion of items which in the present study refers to documents. It can be stated that the results regarding the suggested content are consistent and compatible with the literature in the area of Recommender Systems. It is noteworthy that the development of distributed systems contributes to the area in question since performance against large volumes of information is crucial in order to produce products that can assist users in their choice

Recommendations based on social links

The goal of this chapter is to give an overview of recent works on the development of social link-based recommender systems and to offer insights on related issues, as well as future directions for research. Among several kinds of social recommendations, this chapter focuses on recommendations, which are based on users’ self-defined (i.e., explicit) social links and suggest items, rather than people of interest. The chapter starts by reviewing the needs for social link-based recommendations and studies that explain the viability of social networks as useful information sources. Following that, the core part of the chapter dissects and examines modern research on social link-based recommendations along several dimensions. It concludes with a discussion of several important issues and future directions for social link-based recommendation research