The fast development of event-based social networks (EBSN) provides a convenient platform for recruiting offline participants via online event announcements. Given its ever-increasing new events, how to accurately recommend users their most preferred ones is a key to the success of an EBSN. In this paper, we propose a semantic-enhanced and context-aware hybrid collaborative filtering for event recommendation, which combines semantic content analysis and contextual event influence for user neighborhood selection. In particular, we first exploit the latent topic model for analyzing event description text and establish each user a long-term interest model and short-term interest model from her event registration history. We next establish each event an influence weight to jointly represent its social impact among users and its semantic uniqueness among events. For one user, we select her neighbors according to their longterm interest similarities weighted by events' influences. For new event recommendation, we construct a user-event rating matrix based on users' short-term interest models and for each user, we compute event rating predictions from her neighbors' ratings. The experiments based on the real-world dataset demonstrate the superiority of our algorithm over the peer schemes. INDEX TERMS Hybrid collaborative filtering, event semantic analysis, event influence weight, event recommendation, event-based social networks.
This study investigated the impact of telephone answering machines on telephone survey participation. The study found that households with answering machines were more likely to be contacted, more likely to complete the interview, and less likely to refuse to participate in the study compared to households where there was no answer on the initial call attempt. The study also investigated the utility of leaving messages on the answering machine as a means of encouraging participation. While leaving messages did result in higher participation rates, there were no significant differences among the types of messages tested.
There is no doubt that the COVID-19 epidemic posed the most significant challenge to all governments globally since January 2020. People have to readapt after the epidemic to daily life with the absence of an effective vaccine for a long time. The epidemic has led to society division and uncertainty. With such issues, governments have to take efficient procedures to fight the epidemic. In this paper, we analyze and discuss two official news agencies’ tweets of Iran and Turkey by using sentiment- and semantic analysis-based unsupervised learning approaches. The main topics, sentiments, and emotions that accompanied the agencies’ tweets are identified and compared. The results are analyzed from the perspective of psychology, sociology, and communication.
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