Information technology and the popularity of mobile devices allow for various types of customer data, such as purchase history and behavior patterns, to be collected. As customer data accumulate, the demand for recommender systems that provide customized services to customers is growing. Global e-commerce companies offer recommender systems to gain a sustainable competitive advantage. Research on recommender systems has consistently suggested that customer satisfaction will be highest when the recommendation algorithm is accurate and recommends a diversity of items. However, few studies have investigated the impact of accuracy and diversity on customer satisfaction. In this research, we seek to identify the factors determining customer satisfaction when using the recommender system. To this end, we develop several recommender systems and measure their ability to deliver accurate and diverse recommendations and their ability to generate customer satisfaction with diverse data sets. The results show that accuracy and diversity positively affect customer satisfaction when applying a deep learning-based recommender system. By contrast, only accuracy positively affects customer satisfaction when applying traditional recommender systems. These results imply that developers or managers of recommender systems need to identify factors that further improve customer satisfaction with the recommender system and promote the sustainable development of e-commerce.
The advertising market’s use of smartphones and kiosks for non-face-to-face ordering is growing. An advertising video recommender system is needed that continuously shows advertising videos that match a user’s taste and displays other advertising videos quickly for unwanted advertisements. However, it is difficult to make a recommender system to identify users’ dynamic preferences in real time. In this study, we propose an advertising video recommendation procedure based on computer vision and deep learning, which uses changes in users’ facial expressions captured at every moment. Facial expressions represent a user’s emotions toward advertisements. We can utilize facial expressions to find a user’s dynamic preferences. For such a purpose, a CNN-based prediction model was developed to predict ratings, and a SIFT algorithm-based similarity model was developed to search for users with similar preferences in real time. To evaluate the proposed recommendation procedure, we experimented with food advertising videos. The experimental results show that the proposed procedure is superior to benchmark systems such as a random recommendation, an average rating approach, and a typical collaborative filtering approach in recommending advertising videos to both existing users and new users. From these results, we conclude that facial expressions are a critical factor for advertising video recommendations and are helpful in properly addressing the new user problem in existing recommender systems.
For one company to have a competitive advantage and sustainability over others, its human resource management is of the utmost importance to secure competent employees. As job satisfaction plays a critical role in securing excellent manpower and enhancing corporate performance, it is essential to identify factors that would affect employees’ job satisfaction. Recently, writing reviews with integrity on job portal sites by former and current employees has become prevalent as such websites have guaranteed the reviewers’ anonymity. For this reason, we collected a vast amount of review data over nine industries, such as IT web communication, from one of the representative job portal sites in South Korea, Job Planet, and investigated factors that affect one’s job satisfaction based on the two-factor theory. As a result, it was found that (1) both motivation and hygiene factors had a substantial effect on job satisfaction over all industries; (2) the moderating effect between former and current employees was different for each industry; and (3) there was no moderating effect on job satisfaction between motivation and hygiene factors.
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