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Geographical Adaptive Attention Model for Points of Interest Recommendation
Muyeon Jo, Sejin Chun, Jungkyu Han
http://doi.org/10.5626/JOK.2025.52.3.217
Geographical influence, stemming from the location of Points of Interest (POIs), plays a vital role in POI recommendation. Most current studies utilize geographical information such as distance and location to define and extract POI-specific geographical influences for personalized recommendations. These approaches primarily emphasize distance-based influence, which gauges user preferences based on proximity, while often overlooking area-based influence, which reflects preferences for regions with specific POI characteristics. This paper introduces a POI recommendation model based on an attention network that integrates both distance- and area-based influences. The model adaptively assesses how previously visited POIs impact the likelihood of visiting a target POI, taking into account regional characteristics and user preferences. Experiments conducted on real-world datasets indicate that the proposed method significantly outperforms baseline models, achieving improvements of approximately 6–12% in Prec@10, 8–10% in Recall@10, and 6–7% in HR@10.
Instagram User Embedding and Fashion Photo Recommendation Using "likes" of Fashion Photos
http://doi.org/10.5626/JOK.2021.48.11.1235
As individual preference of fashion styles diversifies, demands for research recommending personalized fashion are increasing. Recently, with the development of deep learning technology, many studies have been conducted using deep learning to extract features from fashion photos and use them for recommendations. In this work, we exploit social network data to consider users and fashion styles in recommending fashion photos. Since social network users tend to post fashion photos in their preferred style and tag them with “Like“, social network data are very important for understanding relationship between users and fashion photos. We propose a technique to map users and fashion photos into the same vector space using social network data structure which consists of users and fashion photos. Especially, it is possible to use our method to recommend fashion photos that a user might prefer by mapping users and fashion photos not used for learning into a vector space without additional learning.
Disassortative Network Distribution Techniques Using Hub Grouping Based On Local Differential Privacy
http://doi.org/10.5626/JOK.2020.47.6.603
With the development of the wireless Internet and popularization of smartphones, many people are using social network services that connect with others in online. Personal data generated by social network services have high value, but comprise sensitive personal information that could potentially result in serious privacy breaches. The existing studies have presented techniques for generating synthetic data similar to the original network data, or anonymous user information. However, the existing techniques have inherent weaknesses in privacy and data utility because such techniques have not considered the characteristics of network graphs formed by relationships with users. In this paper, we propose the privacy-protected social network data distribution techniques by applying local differential privacy techniques that reflect the characteristics on the social network graph. Through experiments with real data, we have shown that the proposed techniques perform better than the existing differentially private social network data distribution techniques.
Detection of Malicious Users with High Influence through Foul Language Network Analysis in MOBA Games
http://doi.org/10.5626/JOK.2018.45.12.1312
In relation to the online game industry, verbal violence in the game has become a serious social problem. However, it is difficult to solve fundamental problems by simply filtering or using reporting systems. This study proposed a method to analyze the propagation tendency of the foul language and to detect malicious users in social network perspective. This method was applied to the analysis of the chat log of Defense of the Ancients 2(DotA 2), a popular MOBA(Multiplayer Online Battle Arena) genre game around the world. In the case of MOBA games, there are usually limited users belonging to one queue, which is a good platform for analyzing foul language networks as compared to other games. Verbally abusive malicious users tend to have high centrality when they form a network. Using these features, we analyzed the propagation tendency of the foul language on the network and detected users with high centrality. We also analyzed the effect on the whole network when the user was restricted. With the proposed method, we were able to detect malicious users who used the foul language. For future works, we will classify the spreading types in the foul language network and analyze users for each type.
Opinion Classification in Professional Sports Fan Sites using Topic Keyword-Based Sentiment Analysis
Hyungho Byun, Sihyun Jeong, Chong-kwon Kim
http://doi.org/10.5626/JOK.2018.45.4.390
In this study, we propose the classification method using topic keyword-based sentiment analysis through the posts of professional sports fan sites in Korea. We studied ways to take into account the use of special communication methods or vocabulary in the community and defined keywords based on the characteristics of the topic or frequency of the community"s words. In addition, we presented a new sentiment analysis approach that utilizes the use of keyword pools and the proximity relation to keywords. Through three years of actual community dataset, sentiment analysis based on the topic keyword is more effective than the existing method and reflects the community environment.
Social Network Spam Detection using Recursive Structure Features
Boyeon Jang, Sihyun Jeong, Chongkwon Kim
http://doi.org/10.5626/JOK.2017.44.11.1231
Given the network structure in online social network, it is important to determine a way to distinguish spam accounts from the network features. In online social network, the service provider attempts to detect social spamming to maintain their service quality. However the spammer group changes their strategies to avoid being detected. Even though the spammer attempts to act as legitimate users, certain distinguishable structural features are not easily changed. In this paper, we investigate a way to generate meaningful network structure features, and suggest spammer detection method using recursive structural features. From a result of real-world dataset experiment, we found that the proposed algorithm could improve the classification performance by about 8%.
Fast Influence Maximization in Social Networks
Yun-Yong Ko, Kyung-Jae Cho, Sang-Wook Kim
http://doi.org/10.5626/JOK.2017.44.10.1105
Influence maximization (IM) is the problem of finding a seed set composed of k nodes that maximizes the influence spread in social networks. However, one of the biggest problems of existing solutions for IM is that it takes too much time to select a k-seed set. This performance issue occurs at the micro and macro levels. In this paper, we propose a fast hybrid method that addresses two issues at micro and macro levels. Furthermore, we propose a path-based community detection method that helps to select a good seed set. The results of our experiment with four real-world datasets show that the proposed method resolves the two issues at the micro and macro levels and selects a good k-seed set.
Rank Correlation Coefficient of Energy Data for Identification of Abnormal Sensors in Buildings
Naeon Kim, Sihyun Jeong, Boyeon Jang, Chong-Kwon Kim
Anomaly detection is the identification of data that do not conform to a normal pattern or behavior model in a dataset. It can be utilized for detecting errors among data generated by devices or user behavior change in a social network data set. In this study, we proposed a new approach using rank correlation coefficient to efficiently detect abnormal data in devices of a building. With the increased push for energy conservation, many energy efficiency solutions have been proposed over the years. HVAC (Heating, Ventilating and Air Conditioning) system monitors and manages thousands of sensors such as thermostats, air conditioners, and lighting in large buildings. Currently, operators use the building’s HVAC system for controlling efficient energy consumption. By using the proposed approach, it is possible to observe changes of ranking relationship between the devices in HVAC system and identify abnormal behavior in social network.
Performance Evaluation of Review Spam Detection for a Domestic Shopping Site Application
As the number of customers who write fake reviews is increasing, online shopping sites have difficulty in providing reliable reviews. Fake reviews are called review spam, and they are written to promote or defame the product. They directly affect sales volume of the product; therefore, it is important to detect review spam. Review spam detection methods suggested in prior researches were only based on an international site even though review spam is a widespread problem in domestic shopping sites. In this paper, we have presented new review features of the domestic shopping site NAVER, and we have applied the formerly introduced method to this site for performing an evaluation.
Hybrid Recommendation System of Qualitative Information Based on Content Similarity and Social Affinity Analysis
Recommendation systems play a significant role in providing personalized information to users, with enhanced satisfaction and reduced information overload. Since the mid-1990s, many studies have been conducted on recommendation systems, but few have examined the recommendations of information from people in the online social networking environment. In this paper, we present a hybrid recommendation method that combines both the traditional system of content-based techniques to improve specialization, and the recently developed system of social network-based techniques to best overcome a few limitations of the traditional techniques, such as the cold-start problem. By suggesting a state-of-the-art method, this research will help users in online social networks view more personalized information with less effort than before.
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