9,021 research outputs found

    Adversarial Training Towards Robust Multimedia Recommender System

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    With the prevalence of multimedia content on the Web, developing recommender solutions that can effectively leverage the rich signal in multimedia data is in urgent need. Owing to the success of deep neural networks in representation learning, recent advance on multimedia recommendation has largely focused on exploring deep learning methods to improve the recommendation accuracy. To date, however, there has been little effort to investigate the robustness of multimedia representation and its impact on the performance of multimedia recommendation. In this paper, we shed light on the robustness of multimedia recommender system. Using the state-of-the-art recommendation framework and deep image features, we demonstrate that the overall system is not robust, such that a small (but purposeful) perturbation on the input image will severely decrease the recommendation accuracy. This implies the possible weakness of multimedia recommender system in predicting user preference, and more importantly, the potential of improvement by enhancing its robustness. To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning. The idea is to train the model to defend an adversary, which adds perturbations to the target image with the purpose of decreasing the model's accuracy. We conduct experiments on two representative multimedia recommendation tasks, namely, image recommendation and visually-aware product recommendation. Extensive results verify the positive effect of adversarial learning and demonstrate the effectiveness of our AMR method. Source codes are available in https://github.com/duxy-me/AMR.Comment: TKD

    A Case Study Of E-Supply Chain & Business Process Reengineering Of A Semiconductor Company In Malaysia

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    Penglibatan e-perniagaan dalam rantaian bekalan telah mewujudkan e-rantaian bekalan yang baru (e-SC) di firma-firma tempatan dan global. Due to globalization and advancement in information technology (IT), companies adopt best practices in e-business and supply chain management to be globally competitive as both are realities and prospects in 21st century

    Business process re-engineering (BPR): The REBUS approach

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    Many organisations undertake business process re-engineering (BPR) projects in order to improve efficiency and reduce costs. Although this approach can result in significant improvements and benefits, there are high risks associated with radical changes of business processes and the failure rate of BPR projects is reported to be as high as 70%. The Centre for Re-engineering Business Processes (REBUS) was established at Brunel University to provide a multidisciplinary environment for research into BPR and its success factors. This paper describes the REBUS approach to research concerning the success of BPR projects and presents examples of some of the projects carried out

    Hierarchical Attention Network for Visually-aware Food Recommendation

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    Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is influenced by many factors such as the ingredients, appearance of the recipe, the user's personal preference on food, and various contexts like what had been eaten in the past meals. In this work, we formulate the food recommendation problem as predicting user preference on recipes based on three key factors that determine a user's choice on food, namely, 1) the user's (and other users') history; 2) the ingredients of a recipe; and 3) the descriptive image of a recipe. To address this challenging problem, we develop a dedicated neural network based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of: 1) capturing the collaborative filtering effect like what similar users tend to eat; 2) inferring a user's preference at the ingredient level; and 3) learning user preference from the recipe's visual images. To evaluate our proposed method, we construct a large-scale dataset consisting of millions of ratings from AllRecipes.com. Extensive experiments show that our method outperforms several competing recommender solutions like Factorization Machine and Visual Bayesian Personalized Ranking with an average improvement of 12%, offering promising results in predicting user preference for food. Codes and dataset will be released upon acceptance

    Knowledge Management Applied To Business Process Reengineering

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    The purpose of this paper is to highlight the role of knowledge management (KM) as a critical factor for the business process reengineering (BPR) success. It supports the theory that the knowledge management can supply the dynamic necessary to stimulate successful reengineering and minimize the failure rate and its sources. Implementing KM strategy in reengineering projects will lead to better outcomes, building the support for long-term success into the design of business systems and processes

    Family Breast Cancer Education: A Model for African American Women

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    The purpose of this study, funded by the American Cancer Society, was to increase knowledge and understanding, i.e., the willingness and ability to discuss, of breast cancer in southern minority women and their families. A family model of health education guided the research questions. (a) To what extent will an action research intervention increase knowledge about the causes and treatment of breast cancer in minority women? (b) To what extent will an action research intervention increase willingness to talk with family members? The t-test analysis of a 67-item, self- administered survey indicated significant increases in knowledge of cancer and in their willingness to talk with family members about breast cancer. In addition, they reported increases in comfort level about discussing breast cancer as well as willingness to talk with others about their own (possible) positive diagnosis. We infer that increased comfort level and willingness to talk with others has a relationship to increased awareness of breast cancer
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