Search results

Online news websites provide huge amounts of timely news, bringing the challenge of recommending personalized news articles. Generative adversarial networks (GAN) based on collaborative filtering (CFGAN) can achieve effective recommendation quality. However, CFGAN ignores item contents, which contain more latent preference features than just user ratings. It is important to consider both ratings and item contents in making preference predictions. This study aims to improve news recommendation by proposing a GAN-based news recommendation model considering both ratings (implicit feedback) and the latent features of news content.

The collaborative topic modeling (CTM) can improve user preference prediction by combining matrix factorization (MF) with latent topics of item content derived from latent topic modeling. This study proposes a novel hybrid news recommendation model, Hybrid-CFGAN, which modifies the architecture of the CFGAN model with enhanced preference learning from the CTM. The proposed Hybrid-CFGAN model contains parallel neural networks – original rating-based preference learning and CTM-based preference learning, which consider both ratings and news content with user preferences derived from the CTM model. A tunable parameter is used to adjust the weights of the two preference learnings, while concatenating the preference outputs of the two parallel neural networks.

This study uses the dataset collected from an online news website, NiusNews, to conduct an experimental evaluation. The results show that the proposed Hybrid-CFGAN model can achieve better performance than the state-of-the-art GAN-based recommendation methods. The proposed novel Hybrid-CFGAN model can enhance existing GAN-based recommendation and increase the performance of preference predictions on textual content such as news articles.

As the existing CFGAN model does not consider content information and solely relies on history logs, it may not be effective in recommending news articles. Our proposed Hybrid-CFGAN model modified the architecture of the CFGAN generator by adding a parallel neural network to gain the relevant information from news content and user preferences derived from the CTM model. The novel idea of adjusting the preference learning from two parallel neural networks – original rating-based preference learning and CTM-based preference learning – contributes to improve the recommendation quality of the proposed model by considering both ratings and latent preferences derived from item contents. The proposed novel recommendation model can improve news recommendation, thereby increasing the commercial value of news media platforms.

In this “Info-plosion” era, recommendation systems (or recommenders) play a significant role in finding interesting items in the surge of online digital activity and e-commerce. The purpose of this paper is to model users' preference evolution to recommend potential items which users may be interested in.

A novel recommendation system, namely evolution-learning recommendation (ELR), is developed to precisely predict user interest for making recommendations. Differing from prior related methods, the authors integrate the matrix factorization (MF) and recurrent neural network (RNN) to effectively describe the variation of user preferences over time.

A novel cumulative factorization technique is proposed to efficiently decompose a rating matrix for discovering latent user preferences. Compared to traditional MF-based methods, the cumulative MF could reduce the utilization of computation resources. Furthermore, the authors depict the significance of long- and short-term effects in the memory cell of RNN for evolution patterns. With the context awareness, a learning model, V-LSTM, is developed to dynamically capture the evolution pattern of user interests. By using a well-trained learning model, the authors predict future user preferences and recommend related items.

Based on the relations among users and items for recommendation, the authors introduce a novel concept, virtual communication, to effectively learn and estimate the correlation among users and items. By incorporating the discovered latent features of users and items in an evolved manner, the proposed ELR model could promote “right” things to “right” users at the “right” time. In addition, several extensive experiments are performed on real datasets and are discussed. Empirical results show that ELR significantly outperforms the prior recommendation models. The proposed ELR exhibits great generalization and robustness in real datasets, including e-commerce, industrial retail and streaming service, with all discussed metrics.

This paper aims to develop high-pass (HP) negative group delay (NGD) investigation based on three-port lumped circuit. The main particularity of the proposed three-port passive topology is the consideration of only a single circuit element represented by a capacitor.

The methodology of the paper is to consider the S-matrix equivalent model derived from admittance matrix approach. So, an S-matrix equivalent model of a three-port circuit topology is established from admittance matrix approach. The frequency-dependent basic expressions are explored to perform the HP-NGD analysis. Then, the existence condition of HP-NGD function type is analytically demonstrated. The specific characteristics and synthesis equations of HP-NGD circuit with respect to the desired optimal NGD value are established.

After computing the frequency expressions to perform the HP-NGD analysis, this study demonstrated the existence condition of HP-NGD function type analytically. The validity of the HP-NGD theory is verified by a prototype of three-port circuit. The proof-of-concept (POC) single capacitor three-port circuit presents an NGD response and characteristics from analytical calculation and simulation is in very good correlation.

An innovative theory of HP-NGD three-port circuit is studied. The proposed HP-NGD topology is constituted by only a single capacitor. After the topological description, the S-matrix model is established from the Y-matrix by means of Kirchhoff voltage law and Kirchhoff current law equations. A POC of single capacitor three-port circuit was designed and simulated with a commercial tool. Then, a prototype with a surface-mounted device component was fabricated and tested. As expected, simulation and measurement results in very good agreement with the calculated model show the feasibility of the HP-NGD behavior. This work is compared to other NGD-type function with diverse number of ports and components.

Social media has emerged as a prominent platform for marketers and brands to disseminate brand-related information. This study aims to investigate the impact of color congruence between themes and background on marketing effectiveness, focusing specifically on Instagram.

A laboratory experiment was conducted to investigate how color congruence between themes and background in brand posts influences flow and aesthetic experience, subsequently affecting marketing communication parameters such as brand attitude, visit intention, and eWOM on Instagram. Moreover, Adidas Originals was selected as the focal brand, and blue and white color was chosen as the primary color palette for the experimental material.

This study demonstrates that color congruence, regardless of brand layout or post, significantly influences flow and aesthetic experience, subsequently affecting marketing effectiveness.

This study contributes to the theoretical understanding of congruence theory and social media marketing, providing valuable insights for brands to enhance their communication through photographs and effectively manage their official Instagram accounts.

The use of gamification might offer a partial solution to the decline in students' motivation and engagement the school system is currently facing. However, there has been a lack of empirical evidence on whether and how gamification may be able to improve learning motivation. Specifically, this study aims at exploring the antecedents and consequences of intrinsic learning motivation and its mediating role in gamification context.

A survey method was used to gather the information from students, and regression analysis was used to examine the relationship between three categories of gamification features (social, achievement and immersion), and intrinsic learning motivation. Moreover, this study further used self-determination theory to clarify how students' psychological needs (relatedness, competence and autonomy) mediate the effects of gamification and learning motivation.

Results showed that social, achievement and immersion features are key aspects that impact students' intrinsic motivation. The psychological needs of students can further enhance these relationships.

Overall, the findings illuminate the unique motivations for learning and reveal design strategies that can be implemented to improve teaching through gamification features.

The adverse effects on environmental sustainability, human health and animal welfare are often cited as the main reasons for reducing animal-based meat production and consumption. This study explored the food choice motives that determine consumer attitude toward plant-based meat (PBM) as a sustainable meat alternative. The theory of planned behavior (TPB) was applied to further determine whether an individual’s attitude toward PBM, subjective norms and perceived behavioral control influence their willingness to try novel meat substitutes (i.e. PBM). Finally, the moderating effect of meat attachment was also considered.

Online self-reported questionnaires were administered in Taiwan, and 294 valid questionnaires were collected. Structural equation modeling (SEM) and moderated regression were employed for analysis.

The results clarified the food choice motives that influenced consumer attitude toward PBM and revealed that attitude and subjective norms pertaining to trying PBM explained up to 35.03% of the variance in consumer willingness to try PBM. Notably, consumer meat attachment moderated the positive relationship between consumer attitude toward PBM and willingness to try PBM such that it became negative.

On the basis of the empirical findings regarding the food choice motives that influence consumer attitude and willingness to try PBM, this study provided practical implications for marketers seeking to increase consumer willingness to try PBM.

The main theoretical contribution of this research is that food choice motives should be considered in a TPB model to explain consumer willingness to try PBM. The moderating effect of consumer meat attachment should also be considered.

The adoption of mobile payment remains low in certain regions, highlighting the need to identify the factors that enable and inhibit its adoption. This study aims to address this gap by investigating the role of information security, loss aversion and the moderating influence of the herd effect on Inertia and behavioral intentions in the adoption of mobile payment systems.

A structural equation model was developed and tested with 332 valid questionnaires to examine the proposed hypotheses.

The empirical results reveal that information security plays a significant role as an enabler, while loss aversion acts as an inhibitor of mobile payment adoption. Furthermore, the study uncovers the moderating influence of the herd effect on the relationship between Inertia and behavioral intentions.

This study was conducted in a specific region and may not be generalizable to other regions. Future studies could expand the sample size and scope to enhance the external validity of the findings.

This study offers practical implications for mobile payment service providers. Understanding the key enabling and inhibiting factors identified in this study can guide providers in designing and improving their services. Strengthening information security measures can help build trust among potential adopters, while offering incentives can mitigate the impact of loss aversion and encourage early adoption.

The findings of this study have social implications as they contribute to promoting the adoption of mobile payment systems. Increased adoption can enhance financial inclusion and stimulate economic development.

This study provides novel insights into the enabling and inhibiting factors of mobile payment adoption and highlights the moderating role of the herd effect. By shedding light on the influence of social norms on individual behavior in the context of mobile payment adoption, this study contributes to the existing literature and advances our understanding of this phenomenon.

The purpose of this study is to propose the research model integrating the expectation-confirmation model with the views of learning engagement (LE) and extending DeLone and McLean information systems (IS) success model to examine whether quality determinants as antecedents to students' beliefs can influence students' continuance intention of massive open online courses (MOOCs).

Sample data for this study were collected from students enrolled in a comprehensive university in Taiwan. A total of 600 questionnaires were distributed, and 363 (60.5%) useable questionnaires were analyzed using structural equation modeling in this study.

This study proved that students' perceived knowledge quality, system quality, interface design quality, learner–instructor interaction quality, and collaboration quality all positively caused students' perceived usefulness, confirmation and LE in MOOCs, which jointly explained students' satisfaction with MOOCs and subsequently resulted in students' continuance intention of MOOCs.

This study fully evaluates IS-related and interaction-related quality determinants via an understanding of students' state of LE in explaining students' continuance intention of MOOCs that is difficult to expound with only their utilitarian perception of MOOCs. Hence, this study contributes to deep insights into an all-round quality evaluation in the field of MOOCs continuance intention and takes extrinsic and intrinsic motivators into account in the theoretical development of MOOCs continuance intention to acquire a more comprehensive and robust analysis.

This study aimed to identify a specific taxonomy of entry modes for international construction contractors and to develop a decision-making mechanism based on case-based reasoning (CBR) to facilitate the selection of the most suitable entry modes.

According to the experience orientation of the construction industry, a CBR entry mode decision model was established, and based on successful historical cases, a two-step refinement process was carried out to identify similar situations. Then the validity of the model is proved by case analysis.

This study identified an entry mode taxonomy for international construction contractors (ICCs) and explored their decision-making mechanisms. First, a two-dimension model of entry mode for ICCs was constructed from ownership and value chain dimensions; seven common ICC entry modes were identified and ranked according to market commitment. Secondly, this study reveals the impact mechanism of the ICC entry mode from two aspects: the external environment and enterprise characteristics. Accordingly, an entry mode decision model is established.

Firstly, sorting out the categories of entry mode in the construction field, which provide an entry mode list for ICCs to select. Secondly, revealing the impact mechanism of ICC entry mode, which proposes a systematic decision-making system for the selection of ICC entry mode. Thirdly, constructing a CBR entry mode decision-making model from an empirical perspective, which offers tool support and reduces transaction costs in the decision-making process.

The study on entry modes for ICCs is still in the preliminary exploratory stage. The authors investigate the entry mode categories and decision-making mechanisms for ICCs based on Uppsala internationalization process theory. It widens the applied scope of Uppsala and promotes cross-disciplinary integration. In addition, the authors creatively propose a two-stage retrieval mechanism in the CBR model, which considers the order of decision variables. It refines the influence path of the decision variables on ICCs' entry mode.