Hybrid recommender systems for personalized government-to-business e-services

University of Technology, Sydney. Faculty of Engineering and Information Technology.As e-Governments around the world face growing pressures to improve the quality of service delivery and become more efficient and cost-effective, their initiatives currently focus on providing users with a seamless service delivery experience. Webbased technologies offer governments more efficient and effective means than traditional physical channels to provide high quality e-Service delivery to their users, which include citizens and businesses. Government-to-Business (G2B) e-Services involve information distribution, transactions, and interactions with businesses m varying ways via e-Government websites and portals. The G2B e-Services aim to reduce burdens on businesses and to provide effective and efficient access to information for business users. One of the most important e-Services of G2B is the promotion of local businesses goods and services to consumers (i.e., local and overseas businesses) by providing on line business directories. However, with the rapid growth of information and unreliable search facilities, busine s users, who are seeking 'one-to-one' e-Services from government in highly competitive markets, struggle with online business directories and increasingly find it difficult to locate business pa1tners according to their needs and interests. How, then, can business users be provided with inforn1ation and services specific to their needs, rather than an undifferentiated mass of information? An effective solution proposed in this research is the development of personalized G2B e-Services using recommender systems. It is worth mentioning that the adoption of recommender systems in the context of e- Government to provide personalized services has received very limited attention in the literature. Recommender systems aim to suggest the right items (products, services or information) that best match the needs and interests of particular users based on their explicit and implicit preferences. In current recommender systems, the Collaborative Filtering (CF) approaches are the most popular and widely adopted recommendation approaches. Regardless of the success of CF-based approaches in various recommendation applications, they still suffer from data uncertainty, data sparsity, cold-start item and cold-start user problems, resulting in poor recommendation accuracy and reduced coverage. An effective solution proposed in this research to alleviate such problems is the development of hybrid and fusion-based recommendation algorithms that exploit and incorporate additional knowledge about users and items. Such knowledge can be extracted from either the users ' trust social network or the items' semantic domain knowledge. This research explores the adoption of recommender systems m an e- Govemment context for the provision of personalized G2B e-Services. Accordingly, a G2B recommendation framework for providing personalized G2B e-Services (particularly personalized business partner recommendations) for Small-to-Medium Businesses (SMBs) is proposed. Novel hybrid and fusion-based recommendation models and algorithms are also proposed and developed to overcome the limitations of existing CF-based recommendation approaches. Experimental results on real datasets show that our proposed recommendation algorithms significantly outperfmm existing recommendation algorithms in terms of recommendation accuracy and coverage when dealing with data sparsity, cold-start item and cold-start user limitations inherent in CF-based recommendation approaches

An effective recommender system by unifying user and item trust information for B2B applications

© 2015 Elsevier Inc. Although Collaborative Filtering (CF)-based recommender systems have received great success in a variety of applications, they still under-perform and are unable to provide accurate recommendations when users and items have few ratings, resulting in reduced coverage. To overcome these limitations, we propose an effective hybrid user-item trust-based (HUIT) recommendation approach in this paper that fuses the users' and items' implicit trust information. We have also considered and computed user and item global reputations into this approach. This approach allows the recommender system to make an increased number of accurate predictions, especially in circumstances where users and items have few ratings. Experiments on four real-world datasets, particularly a business-to-business (B2B) case study, show that the proposed HUIT recommendation approach significantly outperforms state-of-the-art recommendation algorithms in terms of recommendation accuracy and coverage, as well as significantly alleviating data sparsity, cold-start user and cold-start item problems

The Assessment of Patient Clinical Outcome: Advantages, Models, Features of an Ideal Model

Background: The assessment of patient clinical outcome focuses on measuring various aspects of the health status of a patient who is under healthcare intervention. Patient clinical outcome assessment is a very significant process in the clinical field as it allows health care professionals to better understand the effectiveness of their health care programs and thus for enhancing the health care quality in general. It is thus vital that a high quality, informative review of current issues regarding the assessment of patient clinical outcome should be conducted. Aims & Objectives: 1) Summarizes the advantages of the assessment of patient clinical outcome; 2) reviews some of the existing patient clinical outcome assessment models namely: Simulation, Markov, Bayesian belief networks, Bayesian statistics and Conventional statistics, and Kaplan-Meier analysis models; and 3) demonstrates the desired features that should be fulfilled by a well-established ideal patient clinical outcome assessment model. Material & Methods: An integrative review of the literature has been performed using the Google Scholar to explore the field of patient clinical outcome assessment. Conclusion: This paper will directly support researchers, clinicians and health care professionals in their understanding of developments in the domain of the assessment of patient clinical outcome, thus enabling them to propose ideal assessment models

Recommendation technique-based government-to-business personalized e-services

One of the new directions in current e-government development is to provide personalized online services to citizens and businesses. Recommendation techniques can bring a possible solution for this issue. This study proposes a hybrid recommendation approach to provide personalized government to business (G2B) e-services. The approach integrates fuzzy sets-based semantic similarity and traditional item-based collaborative filtering methods to improve recommendation accuracy. A recommender system named Intelligent Business Partner Locator (IBPL) is designed to apply the proposed recommendation approach for supporting government agencies to recommend business partners. ©2009 IEEE

A Robust Algorithm for Emoji Detection in Smartphone Screenshot Images

The increasing use of smartphones and social media apps for communication results in a massive number of screenshot images. These images enrich the written language through text and emojis. In this regard, several studies in the image analysis field have considered text. However, they ignored the use of emojis. In this study, a robust two-stage algorithm for detecting emojis in screenshot images is proposed. The first stage localizes the regions of candidate emojis by using the proposed RGB-channel analysis method followed by a connected component method with a set of proposed rules. In the second verification stage, each of the emojis and non-emojis are classified by using proposed features with a decision tree classifier. Experiments were conducted to evaluate each stage independently and assess the performance of the proposed algorithm completely by using a self-collected dataset. The results showed that the proposed RGB-channel analysis method achieved better performance than the Niblack and Sauvola methods. Moreover, the proposed feature extraction method with decision tree classifier achieved more satisfactory performance than the LBP feature extraction method with all Bayesian network, perceptron neural network, and decision table rules. Overall, the proposed algorithm exhibited high efficiency in detecting emojis in screenshot images

Vibrant search mechanism for numerical optimization functions

Recently, various variants of evolutionary algorithms have been offered to optimize the exploration and exploitation abilities of the search mechanism.Some of these variants still suffer from slow convergence rates around the optimal solution. In this paper, a novel heuristic technique is introduced to enhance the search capabilities of an algorithm, focusing on certain search spaces during evolution process. Then, employing a heuristic search mechanism that scans an entire space before determining the desired segment of that search space. The proposed method randomly updates the desired segment by monitoring the algorithm search performance levels on different search space divisions. The effectiveness of the proposed technique is assessed through harmony search algorithm (HSA). The performance of this mechanism is examined with several types of benchmark optimization functions, and the results are compared with those of the classic version and two variants of HSA. The experimental results demonstrate that the proposed technique achieves the lowest values (best results) in 80% of the non-shifted functions, whereas only 33.3% of total experimental cases are achieved within the shifted functions in a total of 30 problem dimensions. In 100 problem dimensions, 100% and 25% of the best results are reported for non-shifted and shifted functions, respectively. The results reveal that the proposed technique is able to orient the search mechanism toward desired segments of search space, which therefore significantly improves the overall search performance of HSA, especially for non-shifted optimization functions

A doctor recommender system based on collaborative and content filtering

The volume of healthcare information available on the internet has exploded in recent years. Nowadays, many online healthcare platforms provide patients with detailed information about doctors. However, one of the most important challenges of such platforms is the lack of personalized services for supporting patients in selecting the best-suited doctors. In particular, it becomes extremely time-consuming and difficult for patients to search through all the available doctors. Recommender systems provide a solution to this problem by helping patients gain access to accommodating personalized services, specifically, finding doctors who match their preferences and needs. This paper proposes a hybrid content-based multi-criteria collaborative filtering approach for helping patients find the best-suited doctors who meet their preferences accurately. The proposed approach exploits multi-criteria decision making, doctor reputation score, and content information of doctors in order to increase the quality of recommendations and reduce the influence of data sparsity. The experimental results based on a real-world healthcare multi-criteria (MC) rating dataset show that the proposed approach works effectively with regard to predictive accuracy and coverage under extreme levels of sparsity

A multi-criteria trust-enhanced collaborative filtering algorithm for personalized tourism recommendations

The exponential growth of online information has LED to significant challenges in navigating data overload, particularly in the tourism industry. Travelers are overwhelmed with choices regarding destinations, accommodations, dining, and attractions, making it difficult to select options that best meet their needs. Recommender systems have emerged as a promising solution to this problem, aiding users in decision-making by providing personalized suggestions based on their preferences. Traditional collaborative filtering (CF) methods, however, face limitations, such as data sparsity and reliance on single rating scores, which do not fully capture the complexity of user preferences. This study proposes a hybrid multi-criteria trust-enhanced CF (HMCTeCF) algorithm to improve the accuracy and robustness of tourism recommendations. HMCTeCF improves the quality of recommendations by integrating multi-criteria user preferences with trust relationships among users and between items. Experimental results using real-world datasets, including Restaurants-TripAdvisor and Hotels-TripAdvisor, demonstrate that HMCTeCF outperforms benchmark CF-based recommendation methods. It achieves higher prediction accuracy and coverage rate, effectively addressing the data sparsity problem. This innovative algorithm facilitates a more personalized and enriching travel experience, particularly in scenarios with limited user data. The findings highlight the importance of considering multiple criteria and trust relationships in developing robust recommendation systems for the tourism industry

Control Theoretical Modeling of Trust-Based Decision Making in Food-Energy-Water Management

We propose a hybrid Human-Machine decision making to manage Food-Energy-Water resources. In our system trust among human actors during decision making is measured and managed. Furthermore, such trust is used to pressure human actors to choose among the solutions generated by algorithms that satisfy the community’s preferred trade-offs among various objectives. We model the trust-based loops in decision making by using control theory. In this system, the feedback information is the trust pressure that actors receive from peers. Using control theory, we studied the dynamics of the trust of an actor. Then, we presented the modeling of the change of solution distances. In both scenarios, we also calculated the settling times and the stability using the transfer functions and their Z-transforms as the number of rounds to show whether and when the decision making is finalized