An Efficient and Scalable Recommender System for the Smart Web

This proceeding at: 11th International Conference on Innovations in Information Technology (IIT) Innovations 2015. Special Theme: Smart Cities, Big Data, Sustainable Development. Took place at 2015, November, 01 - 03, in Dubai, United Arab Emirates (IEEE IIT 2015).This work describes the development of a web recommender system implementing both collaborative filtering and content-based filtering. Moreover, it supports two different working modes, either sponsored or related, depending on whether websites are to be recommended based on a list of ongoing ad campaigns or in the user preferences. Novel recommendation algorithms are proposed and implemented, which fully rely on set operations such as union and intersection in order to compute the set of recommendations to be provided to end users. The recommender system is deployed over a real-time big data architecture designed to work with Apache Hadoop ecosystem, thus supporting horizontal scalability, and is able to provide recommendations as a service by means of a RESTful API. The performance of the recommender is measured, resulting in the system being able to provide dozens of recommendations in few milliseconds in a single-node cluster setup.This research work is part of Memento Data Analysis project, co-funded by the Spanish Ministry of Industry, Energy and Tourism with no. TSI-020601-2012-99 and TSI-020110-2009-137.Publicad

Analisa Multithreading Pada Sistem Rekomendasi Menggunakan Metode Collaborative Filtering Dengan Apache Mahout

Apache mahout saat ini melakukan pengembangan sistem rekomendasi yang didalamnya menggunakan metode Collaborative Filtering, namun dalam implementasinya masih memiliki kekurangan didalam waktu pemrosesan yang masih memakan waktu cukup lama untuk memproses data yang berukuran besar. Penelitian ini akan memanfaatkan multithreading untuk mempercepat waktu pemrosesan data menggunakan library Apache Mahout. Dalam penelitian ini, diketahui bahwa adanya multithreading mampu meningkat kinerja dalam pemrosesan waktu eksekusi data pada Apache Mahout. Pengujian yang telah dilakukan dengan jumlah 20 juta data, didapatkan hasil pengujian pada single thread dengan lama waktu pemrosesan datanya selama 2218 detik. Dan pada pengujian untuk 4 thread didapatkan hasil 764 detik, dan kemudian untuk 8 thread didapatkan hasil 691 detik dan pada pengujian untuk 16 thread didapatkan hasil 1097 detik. Dari  berapa pengujian yang telah dilakukan telah membuktikan bahwa multithreading mampu meningkatkan kinerja apache mahout dalam sistem rekomendasi asalkan jumlah thread tidak melebihi kapasitas ukuran thread yang ada di processor

M-Guide: Recommending Systems of Food Centre in Buleleng Regency

Buleleng is Regency located in the North of Bali. Buleleng becomes one of the tourist destinations for both domestic and international tourist to travel. Buleleng Regency is known for its natural attractions. Besides its numbers of tourist attractions, this Regency also presents a lot of food choices, especially special cuisine in Buleleng. Not only has the special cuisine, Buleleng also had many choices of foods from the outside area. The price, the taste, the atmosphere (view), the service and the facilities vary at each centre. So, the tourists who visit Buleleng are confused when they have to choose one. This system stores 140 food courts in the Buleleng area and each of them is grouped into 15 groups based on its territories. Based on this reason, the collaborative method was chosen for this system. Besides the collaborative method, this system also uses the Location-Based Service (LBS) technology that utilizes the Global Positioning System (GPS) in its application, which the users can find out their position, define and search for specific locations either far or near; one of them is finding the food centre in the area of Buleleng Regency. This system is running on an Android mobile device. This system is expected to (1) facilitate the user in searching the food centre in the area of Buleleng Regency (2) the user can find out the nearest food centre from their location

User modeling for exploratory search on the Social Web. Exploiting social bookmarking systems for user model extraction, evaluation and integration

Exploratory search is an information seeking strategy that extends be- yond the query-and-response paradigm of traditional Information Retrieval models. Users browse through information to discover novel content and to learn more about the newly discovered things. Social bookmarking systems integrate well with exploratory search, because they allow one to search, browse, and filter social bookmarks. Our contribution is an exploratory tag search engine that merges social bookmarking with exploratory search. For this purpose, we have applied collaborative filtering to recommend tags to users. User models are an im- portant prerequisite for recommender systems. We have produced a method to algorithmically extract user models from folksonomies, and an evaluation method to measure the viability of these user models for exploratory search. According to our evaluation web-scale user modeling, which integrates user models from various services across the Social Web, can improve exploratory search. Within this thesis we also provide a method for user model integra- tion. Our exploratory tag search engine implements the findings of our user model extraction, evaluation, and integration methods. It facilitates ex- ploratory search on social bookmarks from Delicious and Connotea and pub- lishes extracted user models as Linked Data

Personalization on E-Content Retrieval Based on Semantic Web Services

In the current educational context there has been a significant increase in learning object repositories (LOR), which are found in large databases available on the hidden web. All these information is described in any metadata labeling standard (LOM, Dublin Core, etc). It is necessary to work and develop solutions that provide efficiency in searching for heterogeneous content and finding distributed context. Distributed information retrieval, or federated search, attempts to respond to the problem of information retrieval in the hidden Web. Multi-agent systems are known for their ability to adapt quickly and effectively to changes in their environment. This study presents a model for the development of digital content retrieval based on the paradigm of virtual organizations of agents using a Service Oriented Architecture. The model allows the development of an open and flexible architecture that supports the services necessary to dynamically search for distributed digital content. A major challenge in searching and retrieving digital content is also to efficiently find the most suitable content for the users. This model proposes a new approach to filtering the educational content retrieved based on Case-Based Reasoning (CBR). It is based on the model AIREH (Architecture for Intelligent Recovery of Educational content in Heterogeneous Environments), a multi-agent architecture that can search and integrate heterogeneous educational content through a recovery model that uses a federated search. The model and the technologies presented in this research exemplify the potential for developing personalized recovery systems for digital content based on the paradigm of virtual organizations of agents. The advantages of the proposed architecture, as outlined in this article, are its flexibility, customization, integrative solution and efficiency

O impacto da inteligência artificial no negócio eletrónico

Pela importância que a Inteligência Artificial exibe na atualidade, revela-se de grande interesse verificar até que ponto ela está a transformar o Negócio Eletrónico. Para esse efeito, delineou-se uma revisão sistemática com o objetivo de avaliar os impactos da proliferação destes instrumentos. A investigação empreendida pretendeu identificar artigos científicos que, através de pesquisas realizadas a Fontes de Dados Eletrónicas, pudessem responder às questões de investigação implementadas: a) que tipo de soluções, baseadas na Inteligência Artificial (IA), têm sido usadas para melhorar o Negócio Eletrónico (NE); b) em que domínios do NE a IA foi aplicada; c) qual a taxa de sucesso ou fracasso do projeto. Simultaneamente, tiveram de respeitar critérios de seleção, nomeadamente, estar escritos em inglês, encontrarem-se no intervalo temporal 2015/2021 e tratar-se de estudos empíricos, suportados em dados reais. Após uma avaliação de qualidade final, procedeu-se à extração dos dados pertinentes para a investigação, para formulários criados em MS Excel. Estes dados estiveram na base da análise quantitativa e qualitativa que evidenciaram as descobertas feitas e sobre os quais se procedeu, posteriormente, à sua discussão. A dissertação termina com as conclusão e discussão de trabalhos futuros.Due to the importance that Artificial Intelligence exhibits today, it is of great interest to see to what extent it is transforming the Electronic Business. To this end, a systematic review was designed to evaluate the impacts of the proliferation of these instruments. The research aimed to identify scientific articles that, through research carried out on Electronic Data Sources, could answer the research questions implemented: a) what kind of solutions, based on Artificial Intelligence, have been used to improve the Electronic Business; b) in which areas of the Electronic Business Artificial Intelligence has been applied; c) what the success rate or failure of the project is. At the same time, they must comply with selection criteria, to be written in English, to be found in the 2015/2021-time interval and to be empirical studies supported by actual data. After a final quality evaluation, the relevant data for the investigation were extracted for forms created in MS Excel. These data were the basis of the quantitative and qualitative analysis that evidenced the findings found and on which they were subsequently discussed. The dissertation ends with the conclusion and discussion of future works

Predicting potential customer needs and wants for agile design and manufacture in an industry 4.0 environment

Manufacturing is currently experiencing a paradigm shift in the way that products are designed, produced and serviced. Such changes are brought about mainly by the extensive use of the Internet and digital technologies. As a result of this shift, a new industrial revolution is emerging, termed “Industry 4.0” (i4), which promises to accommodate mass customisation at a mass production cost. For i4 to become a reality, however, multiple challenges need to be addressed, highlighting the need for design for agile manufacturing and, for this, a framework capable of integrating big data analytics arising from the service end, business informatics through the manufacturing process, and artificial intelligence (AI) for the entire manufacturing value chain. This thesis attempts to address these issues, with a focus on the need for design for agile manufacturing. First, the state of the art in this field of research is reviewed on combining cutting-edge technologies in digital manufacturing with big data analysed to support agile manufacturing. Then, the work is focused on developing an AI-based framework to address one of the customisation issues in smart design and agile manufacturing, that is, prediction of potential customer needs and wants. With this framework, an AI-based approach is developed to predict design attributes that would help manufacturers to decide the best virtual designs to meet emerging customer needs and wants predictively. In particular, various machine learning approaches are developed to help explain at least 85% of the design variance when building a model to predict potential customer needs and wants. These approaches include k-means clustering, self-organizing maps, fuzzy k-means clustering, and decision trees, all supporting a vector machine to evaluate and extract conscious and subconscious customer needs and wants. A model capable of accurately predicting customer needs and wants for at least 85% of classified design attributes is thus obtained. Further, an analysis capable of determining the best design attributes and features for predicting customer needs and wants is also achieved. As the information analysed can be utilized to advise the selection of desired attributes, it is fed back in a closed-loop of the manufacturing value chain: design → manufacture → management/service → → → design... For this, a total of 4 case studies are undertaken to test and demonstrate the efficacy and effectiveness of the framework developed. These case studies include: 1) an evaluation model of consumer cars with multiple attributes including categorical and numerical ones; 2) specifications of automotive vehicles in terms of various characteristics including categorical and numerical instances; 3) fuel consumptions of various car models and makes, taking into account a desire for low fuel costs and low CO2 emissions; and 4) computer parts design for recommending the best design attributes when buying a computer. The results show that the decision trees, as a machine learning approach, work best in predicting customer needs and wants for smart design. With the tested framework and methodology, this thesis overall presents a holistic attempt to addressing the missing gap between manufacture and customisation, that is meeting customer needs and wants. Effective ways of achieving customization for i4 and smart manufacturing are identified. This is achieved through predicting potential customer needs and wants and applying the prediction at the product design stage for agile manufacturing to meet individual requirements at a mass production cost. Such agility is one key element in realising Industry 4.0. At the end, this thesis contributes to improving the process of analysing the data to predict potential customer needs and wants to be used as inputs to customizing product designs agilely