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Multiple topics often exist on social media platforms that compete for users' attention. To explore how users’ attention transfers in the context of multitopic competition can help us understand the development pattern of the public attention.

This study proposes the prediction model for the attention transfer behavior of social media users in the context of multitopic competition and reveals the important influencing factors of users' attention transfer. Microblogging features are selected from the dimensions of users, time, topics and competitiveness. The microblogging posts on eight topic categories from Sina Weibo, the most popular microblogging platform in China, are used for empirical analysis. A novel indicator named transfer tendency of a feature value is proposed to identify the important factors for attention transfer.

The accuracy of the prediction model based on Light GBM reaches 91%. It is found that user features are the most important for the attention transfer of microblogging users among all the features. The conditions of attention transfer in all aspects are also revealed.

The findings can help governments and enterprises understand the competition mechanism among multiple topics and improve their ability to cope with public opinions in the complex environment.

The aim of this study is to report on the results of an empirical investigation of the various factors which have significant impacts on the Internet user’s ability to correctly identify a phishing website.

The research participants were Internet users who have had at least some experience of financial transactions over the Internet. This study conducted a quantitative research with the help of a structured survey questionnaire along with three experimental tasks. A total of 621 valid samples were collected and the multiple regression analysis technique was used to deduce the answers to the research question.

The results show that the model is useful and has explanatory power. And adjusted R2 computed as 0.927, means that 92.7 per cent of the variations in the Internet user’s ability to identify phishing website can be explained by the predictors selected for the model.

Future research should account for the Internet user’s general security practices and behaviour, attitude towards online financial activity, risk-taking ability or risk behaviour and their potential effects on Internet users' ability to identify a phishing website.

The implications of this study provide the foundation for future research on the areas that intend to explain the Internet user’s necessity to take protection or avoid risky behaviour while performing financial transaction over the Internet.

This study provides the body of knowledge with an empirical analysis of impact of various factors on an Internet user’s ability to identify phishing websites. The results of this study can help practitioners create a more successful research model and help researchers better understand user behaviour on the Internet.

The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

With the development and application of mobile internet access, social media represented by Weibo, WeChat, etc. has become the main channel for information release and sharing. High-impact users in social networks are key factors stimulating the large-scale propagation of information within social networks. User influence is usually related to the user’s attention rate, activity level, and message content. The paper aims to discuss these issues.

In this paper, the authors focused on Sina Weibo users, centered on users’ behavior and interactive information, and formulated a weighted interactive information network model, then present a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc., the model incorporated the time dimension, through the calculation of users’ attribute influence and interactive influence, to comprehensively measure the user influence of Sina Weibo users.

Compared with other models, the model reflected the dynamics and timeliness of the user influence in a more accurate way. Extensive experiments are conducted on the real-world data set, and the results validate the performance of the approach, and demonstrate the effectiveness of the dynamics and timeliness. Due to the similarity in platform architecture and user behavior between Sina Weibo and Twitter, the calculation model is also applicable to Twitter.

This paper presents a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc.

Selected current and recent work in the area of cognitive modelling is reviewed. Particular attention is paid to user models (that is, the model held by a system of a user). The relevance of this work to information retrieval is assessed and some attempts to include user models in IR systems are discussed. Implications are drawn for future work in IR.

This paper proposes to rethink the concepts of relevance and usefulness and their relation to the theory–practice gap in management research.

On the basis of the cognitive-linguistic relevance theory or inferential pragmatics, supplemented by insights from information science, we define relevance as a general conceptual category, while reserving usefulness for the instrumental application in a particular case.

There is no reason to hold onto the difference between theoretical and practical relevance, nor to distinguish between instrumental and conceptual relevance.

This novel approach will help to clarify the confusion in the field and contribute to a better understanding of the added value of management research.

This research takes the transfer behavior of users from Tencent QQ to WeChat as an example to discuss the wider transfer behavior of social media users on the Internet.

This paper collects data through a combination of offline interviews and online questionnaire surveys, and utilizes data analysis tools to construct structural equation modeling (SEM). Using Statistical Product and Service Solutions (SPSS) Statistics 22.0 and Analysis of Moment Structures (AMOS) 22.0 software with SEM, this study was carried out to provide reasonable statistical support for relevant proposed hypotheses based on 368 effective samples acquired through the questionnaire.

The findings of this study show that subjective norm, transfer experience, social communication, and knowledge acquisition all have significant associations with transfer intention and switching behavior. To be specific, transfer intention exerts a positive association on switching behavior; function setting, privacy protection and personal innovation have a favorable association with transfer intention; transfer cost has a significantly negative relationship with transfer intention and switching behavior; function setting has no important relationship on switching behavior.

The research results provide a reference for improving the viscosity and loyalty of social media users in the new era and resolving the problem of user churn.

This paper aims to explore the users' cross-app behavior characteristics in mobile search and to predict users' cross-app behavior using multi-dimensional information.

This paper presents a longitudinal user experiment in 15 days. This paper recruited 30 participants and collected their mobile phone log data in the whole experiment. The structured diary method was also used to collect contextual information in mobile search.

This study focused on the users' cross-app behavior in mobile search and described cross-app behavior's basic characteristics. Usage of communication app and tool apps could trigger more cross-app behavior in mobile search. The method of cross-app behavior prediction in the mobile search was proposed. Collecting users' more contextual information, such as search tasks, search motivation and other environmental information, can effectively improve the prediction accuracy of cross-app behavior in mobile search.

The future research on cross-app behavior prediction should focus on context information in mobile search. Better prediction of cross-app behavior can reduce the users' interaction burden.

This paper contributes to research into cross-app behavior, especially in the mobile search research domain.

Session-based recommendation aims to predict the user's next preference based on the user's recent activities. Although most existing studies consider the global characteristics of items, they only learn the global characteristics of items based on a single connection relationship, which cannot fully capture the complex transformation relationship between items. We believe that multiple relationships between items in learning sessions can improve the performance of session recommendation tasks and the scalability of recommendation models. At the same time, high-quality global features of the item help to explore the potential common preferences of users.

This work proposes a session-based recommendation method with a multi-relation global context–enhanced network to capture this global transition relationship. Specifically, we construct a multi-relation global item graph based on a group of sessions, use a graded attention mechanism to learn different types of connection relations independently and obtain the global feature of the item according to the multi-relation weight.

We did related experiments on three benchmark datasets. The experimental results show that our proposed model is superior to the existing state-of-the-art methods, which verifies the effectiveness of our model.

First, we construct a multi-relation global item graph to learn the complex transition relations of the global context of the item and effectively mine the potential association of items between different sessions. Second, our model effectively improves the scalability of the model by obtaining high-quality item global features and enables some previously unconsidered items to make it onto the candidate list.

With the increasing abundance of network resources and big data, multi-modal information search (MMIS) has been paid more and more attention, but the research results of MMIS are relatively few. This paper attempts to put forward constructive suggestions for the design of multi-modal information system, so that the system can have a better user experience, help users improve the efficiency of obtaining information and optimize the information service mode.

A research model of influencing factors is established by using the TAM (technology acceptance model) theory. The influencing factors of users' multi-modal information search behavior (MMISB) are analyzed by using questionnaire, experiment and the structural equation model. On the basis of this, some suggestions are put forward to build the multi-modal search (MMS) system and improve the efficiency of MMIS.

The research shows that users' MMISB is directly related to their search intention, and the search intention can influence users' cognition of the usefulness and ease of MMIS through their own information search ability and system characteristics. The user's MMIS ability is affected by the demand expression ability and retrieval ability cognition; the user's cognition of system characteristics is affected by the system function and information quality. This shows that the user's MMISB is closely related to the user's cognitive situation, but due to the author's limited time and research ability, only the questionnaire survey method cannot be used to in-depth research and explore the influencing factors of MMIS. Therefore, in the future research, we should combine the interview method to further track the user's emotional factors and scene factors.

For the first time, TAM theory is combined with cross-modal retrieval behavior and the paper explores the influencing factors and evaluation indexes of users' MMISB. The second, the questionnaire was compiled to investigate the influencing factors of the MMISB of the university group, and the reliability analysis, validity analysis, correlation analysis and structural equation model analysis of the survey data are carried out . The survey data and analysis results are original, which can provide a theoretical basis for improving the service level of MMIS.