Hyun Sil Moon

A sequence-based filtering method for exhibition booth visit recommendations

Abstract As exhibitions are known to play important roles in marketing and sales promotion, the exhibition industry has grown significantly not only in the exhibition event size and frequency but also in the number of participating firms and visitors. While the challenge in assessing economic returns from exhibitions is being studied, it is agreed that the eventual success of an exhibition resides largely in its ability to meet the visitors’ needs. Visitors use an exhibition as a source of information when searching for products or services. Though an exhibition provides an information-rich environment, however, visitors often get lost in the abundance of information. A specialized recommender system can be a good solution to information overload as it can guide visitors to right exhibition booths and help them collect necessary information. Traditional collaborative-filtering recommender systems, however, use only customers’ rating or purchase records so that they do not capture exhibition visitors’ temporal visit sequences and dynamic preferences. Moreover, due to the computation overhead, they cannot generate real-time recommendation in ubiquitous environments for exhibitions. In order to overcome these drawbacks, this study proposes a booth recommendation procedure that takes into consideration not only booth visit records but also visit sequences. Experiment results show that the proposed procedure achieves higher recommendation accuracy, faster computation, and more diversity than a typical collaborative-filtering recommender system. From the results, we conclude that the proposed booth recommendation procedure is suitable for real-time recommendation in ubiquitous exhibition environments.

A Network Analysis of Information Exchange using Social Media in ICT Exhibition

The proliferation of using social media and social networking services affects the lifestyles of people. These phenomena are useful to companies that wish to promote and advertise new products or services through these social media; these social media venues also come with large amounts of user data. However, studies that analyze the data of social media within the perspective of information exchanges are hard to find. Much of the previous research in this area is focused on measuring the performance of exhibitions using general statistical approaches and piecemeal measures. Therefore, in this study, we want to analyze the characteristics of information exchanges in social media by using Twitter data sets, which are relating to the Mobile World Congress (MWC). Using this methodology provides exhibition organizers and exhibitors to objectively estimate the effect of social media, and establish strategies with social media use. Through a user network analysis, we additionally found that social attributes are as important as the popular attribute regarding the sustainability of information exchanges. Consequently, this research provides a network analysis using the data derived from the use of social media to communicate information regarding the MWC exhibition, and reveals the significance of social attributes such as the degree and the betweenness centrality regarding the sustainability of information exchanges.

The impact of information amount on the performance of recommender systems

Due to the development of the Internet and smart technology, massive amounts of data with transaction records have been generated by online and offline environments. And the proliferation of items has made it difficult for customers to find the specific items they want to buy. In order to solve this problem, many companies have adopted recommender systems to provide personalization services. However, due to the explosive growth of data, they try to use only meaningful and essential data in order to reduce these costs. And, because recommender systems necessarily deal with personal and sensitive information, some customers are concerned that their private information may be exposed by them. Based on these concerns, in this study, we analyze the effects of the amount of information on the recommendation performance. We assume that a customer could choose to provide overall information or partial information. Using two data sets which are obtained by on-line and off-line environments, we evaluate the difference in the performance of customers who provided overall information and partial information. The experimental results indicated that the recommendation performance for customers who provided overall information generally shows higher accuracy but there are some differences between on-line and off-line environments. Therefore, our study can provide some insight to companies concerning the efficient utilization of data.

Design and Development of POS System Based on Social Network Service

Companies and governments in an era of big data have been tried to create new values with their data resources. Among many data resources, many companies especially pay attention to data which is obtained from Social Network Service (SNS) because it reveals precise opinion of customers and can be used to estimate profiles of them from their social relationships. However, it is not only hard to collect, store, and analyze the data, but system applications are also insufficient. Therefore, this study proposes a S-POS (Social POS) system which consists of three parts; Twitter Side, POS Side and TPAS (Twitter&POS Analysis System). In this system, SNS data and POS data which are collected from Twitter Side and POS Side are stored in Mongo D/B. And it provides several services with POS terminal based on analysis and matching results which are generated from TPAS. Through S-POS system, we expect to efficient and effective store and sales managements of system users. Moreover, they can provide some differentiated services such as cross-selling and personalized recommendation services.

An Exhibition Booth Layout Allocation Procedure Based on Social Network Analysis

As the development of information technology is growing, valuable information ubiquitous can be extracted easily. For the same reason, many exhibition organizers apply ubiquitous technologies in exhibition. These technologies can extract valuable information like visitors path data. To provide a booth recommendation and booth event recommendation have been already studied using the technologies. Booth layout also has a positive effect on maximizing the marketing performance. But there is no proper criteria and research about booth layout for effective visitors visit a booth. To compensate the limitations of existing researches, this paper proposes effective booth layout allocation procedure that is analyzed by social network analysis this paper proposes effective booth layout procedure that is visit network. Furthermore, we suggest their booths in a group are allocated in specific sector. We expect to provide method how to set up a booth to arrange to be able to increase the satisfaction of visitors and exhibitor.