Search results

Providing online news recommendations to users has become an important trend for online media platforms, enabling them to attract more users. The purpose of this paper is to propose an online news recommendation system for recommending news articles to users when browsing news on online media platforms.

A Collaborative Semantic Topic Modeling (CSTM) method and an ensemble model (EM) are proposed to predict user preferences based on the combination of matrix factorization with articles’ semantic latent topics derived from word embedding and latent topic modeling. The proposed EM further integrates an online interest adjustment (OIA) mechanism to adjust users’ online recommendation lists based on their current news browsing.

This study evaluated the proposed approach using offline experiments, as well as an online evaluation on an existing online media platform. The evaluation shows that the proposed method can improve the recommendation quality and achieve better performance than other recommendation methods can. The online evaluation also shows that integrating the proposed method with OIA can improve the click-through rate for online news recommendation.

The novel CSTM and EM combined with OIA are proposed for news recommendation. The proposed novel recommendation system can improve the click-through rate of online news recommendations, thus increasing online media platforms’ commercial value.

Online news websites provide diverse article topics, such as fashion news, entertainment and movie information, to attract more users and create more benefits. Recommending movie information to users reading news online can enhance the impression of diverse information and may consequently improve benefits. Accordingly, providing online movie recommendations can improve users’ satisfactions with the website, and thus is an important trend for online news websites. This study aims to propose a novel online recommendation method for recommending movie information to users when they are browsing news articles.

Association rule mining is applied to users’ news and movie browsing to find latent associations between news and movies. A novel online recommendation approach is proposed based on latent Dirichlet allocation (LDA), enhanced collaborative topic modeling (ECTM) and the diversity of recommendations. The performance of proposed approach is evaluated via an online evaluation on a real news website.

The online evaluation results show that the click-through rate can be improved by the proposed hybrid method integrating recommendation diversity, LDA, ECTM and users’ online interests, which are adapted to the current browsing news. The experiment results also show that considering recommendation diversity can achieve better performance.

Existing studies had not investigated the problem of recommending movie information to users while they are reading news online. To address this problem, a novel hybrid recommendation method is proposed for dealing with cross-type recommendation tasks and the cold-start issue. Moreover, the proposed method is implemented and evaluated online in a real world news website, while such online evaluation is rarely conducted in related research. This work contributes to deriving user’s online preferences for cross-type recommendations by integrating recommendation diversity, LDA, ECTM and adaptive online interests. The research findings also contribute to increasing the commercial value of the online news websites.

In this study, recovery factor was considered in the maintenance management model that integrates the quantitative method and qualitative concept. The model is developed to solve the practical parameters in a maintenance management task, such as the number of maintenance personnel and maintenance cycle time. The stochastic model is applied to construct the relationships among maintenance cycle, maintenance personnel allocation, human recovery factor, and a system's tolerance time. In addition, a simulated experiment was conducted to find out the supplementary parameters such as individual latent human error, individual critical human error, recovery rate, and a system's tolerance time. Since system availability is the criterion of this maintenance management model, the final solution of this model provides a system availability reference table of the combination of the number of maintenance personnel and the maintenance cycle time. This system availability reference table is a practical tool for maintenance managers.

Entrepreneurship, along with its effect on economic growth, has been a major topic of research for quite some time now. However, none of these studies employs the use of entrepreneurial intention, a key indicator of latent entrepreneurs, as a measure of entrepreneurship. Till now, some small-scale studies have been done using survey data, with results indicating that external entrepreneurial environment affects entrepreneurial intention. A handful of studies have also looked at the linkages between economic freedom and entrepreneurial activities. The paper aims to discuss this issue.

Using a panel data setting, this paper investigates the effects of economic freedom, especially regulation, on entrepreneurial intention. The empirical analysis uses data for 79 countries from 2001 to 2012.

The findings suggest that stricter credit market regulation reduces entrepreneurial intention whereas more stringent labor regulations restricts job availability and thereby encourage more people to take up entrepreneurship as a career choice.

The entrepreneurial intention data available from GEM is a highly unbalanced data and the data also does not differentiate between latent entrepreneurship in agricultural and non-agricultural sectors.

Future research should focus more on latent entrepreneurship which is a rough estimate of future entrepreneurs.

Entrepreneurship acts as a channel to improve economic growth by creating more jobs and the institutional qualities might act as a barrier for aspiring entrepreneurs to take up entrepreneurship as their career choices in developing countries.

This study has a twofold contribution in the literature. First, it is the foremost large scale study that deals with entrepreneurial intention using secondary data from Global Economic Monitor (GEM) report. Second, this study explores the linkages between economic freedom index and entrepreneurial intention.

Briefing in a project delivery context is one of the most critical factors in the project success. It defines client requirements, translates these needs into design criteria and generates a design concept. A lack of briefing clarity is one of the main causes of design changes and may lead to project cost and time overruns. This research aims to assess the brief clarity and its influence on project cost and duration.

This research created the PDRI-SD technique by utilising a system dynamic (SD) approach and project definition rating index (PDRI) tool to model the complex system of project briefing and associated variables. Stock and flow diagrams of the main subsystems including the briefing, the detailed design and the construction process, were developed to assess the influence of brief clarity on project cost and time. The PDRI was adopted to measure the briefing clarity and apply in the model. PDRI-SD was then tested in Australian building refurbishment projects to assess the model's effectiveness.

The simulation results indicated that a minor reduction of the lack of clarity throughout the initial briefing process could significantly mitigate unpredicted delay and cost overruns during the detailed design and the construction stage.

This research contributed to the existing body of knowledge by developing an effective technique to measure the impact of lack of brief clarity on project cost and time performance. PDRI-SD can also aid project clients to predict the influence of the initial defined brief on the detailed design and construction process using the historical data of similar previous projects. It provides clients with feedback, indicating whether the brief meets project requirements or whether parts of the project brief require more clarification/rectification before the project handover to the builders.

Multi-criteria recommender systems (MC-RSs) allow users to express their preference in multiple aspects. Bayesian flexible mixture model (BFMM) is a model-based RS which extends FMM from single-criterion to MC. However, results of BFMM have a preference on different rating pattern problem. In single-criterion, FMM with decoupled normalization and W’s transposed function try to solve this problem. However, these techniques are applied to each criterion separately. Then, the relationship among criteria will be lost. This paper aims to solve different rating pattern problems and loss of the relationship between criteria.

The proposed method is combining between BFMM and rating conversion. First, mean and variance normalization is applied to make MC ratings of an active user and a neighbor lying on the same plane. After that, a pattern of each user is extracted using principal component analysis (PCA). Next, the pattern is used to convert neighbors’ MC ratings to the active user aspect. After that, converted MC ratings of neighbors are aggregated to be overall ratings using multiple linear regression (MLR). Finally, overall rating of the active user toward the target item is predicted using weighted average on the derived neighbors’ overall ratings where the similarity from BFMM acts as a weight.

The experimental results show that the proposed method where all criteria ratings are converted simultaneously can improve the performance of recommendation.

The proposed method predicts overall rating of the active user by converting MC ratings of each neighbor to the active user aspect at the same time, which can reduce the loss of the relationship between criteria.

Aspects extracted from the user’s historical records are widely used to define user’s fine-grained preferences for building interpretable recommendation systems. As the aspects were extracted from the historical records, the aspects that represent user’s negative preferences cannot be identified because of their absence from the records. However, these latent aspects are also as important as those aspects representing user’s positive preferences for building a recommendation system. This paper aims to identify the user’s positive preferences and negative preferences for building an interpretable recommendation.

First, high-frequency tags are selected as aspects to describe user preferences in aspect-level. Second, user positive and negative preferences are calculated according to the positive and negative preference model, and the interaction between similar aspects is adopted to address the aspect sparsity problem. Finally, an experiment is designed to evaluate the effectiveness of the model. The code and the experiment data link is: https://github.com/shiyu108/Recommendation-system

Experimental results show the proposed approach outperformed the state-of-the-art methods in widely used public data sets. These latent aspects are also as important as those aspects representing the user’s positive preferences for building a recommendation system.

This paper provides a new approach that identifies and uses not only users’ positive preferences but also negative preferences, which can capture user preference precisely. Besides, the proposed model provides good interpretability.

The use of behavioral insights and experimental methods has recently gained momentum among health policy-makers. There is a tendency, however, to reduce behavioral insights applications in health to “nudges,” and to reduce experiments in health to “randomized controlled trials” (RCTs). We argue that there is much more to behavioral insights and experimental methods in health economics than just nudges and RCTs. First, there is a broad and rich array of complementary experimental methods spanning the lab to the field, and all of them could prove useful in health economics. Second, there are a host of challenges in health economics, policy, and management where the application of behavioral insights and experimental methods is timely and highly promising. We illustrate this point by describing applications of experimental methods and behavioral insights to one specific topic of fundamental relevance for health research and policy: the experimental elicitation and econometric estimation of risk and time preferences. We start by reviewing the main methods of measuring risk and time preferences in health. We then focus on the “behavioral econometrics” approach to jointly elicit and estimate risk and time preferences, and we illustrate its state-of-the-art applications to health.

This study presents the applicability of a model-based approach for loyalty program forecasting using smartphone app in the digital strategy of the retail industry.

The authors develop a dynamic model with the cyclical structure of customer segments through customer experience. They use time-series data on the number of members of the loyalty program, “Seven Mile Program” and confirm the validity of the approximate calculation of customer segment share, customer segment sales share and aggregate sales performance. The authors present three medium-term forecast scenarios after the launch of a smartphone payment service linked with the loyalty program.

The sum of the two customer segment shares for forecasting (the sum of the quasi-excellent and excellent customer ratios) is about 30% in each scenario, consistent with an essential customer loyalty (true loyalty) share obtained in the existing empirical study.

Digital strategy in the retail industry should focus more on estimating and forecasting average amounts of customer segments and the number of aggregated customers through the digitalization on the customer side than on individual customer journeys and responses.

Multi-scenario evaluation through simulation of dynamic models from a systemic view can be used for decision-making in retailing digital strategies.

This study builds a model that integrates the cyclicality of customer segment transition through customer experiences into a loyalty matrix framework, which is a method that has previously been used in the hospitality industry.

The potential for differential functioning of performance assessments across ratings sources has gained recent research interest. This study used multiple-group confirmatory factor analysis (MGCFA) to examine whether measures of task and contextual performance are invariant across both supervisors and subordinates. As an extension, multiple indicators multiple causes modeling (MIMIC) was used to examine potential covariates of task and contextual performance ratings on latent task and contextual performance variability. Consistent with previous research, I found measurement invariance across subordinate- and supervisor ratings. Moreover, MIMIC results showed supervisor and subordinate demographic variables systematically influenced latent task and contextual performance variability despite measurement invariance over these rating sources. Implications for multi-source performance systems are discussed.