• Convergence Security Journal
• /
• v.19 no.2
• /
• pp.73-81
• /
• 2019

In this paper, we propose a user-dependent mid-point navigation system using a time weighted mid-point navigation algorithm and a user preference based mid-point neighborhood recommendation system. The proposed system consists of a mid-point navigation module for calculating an mid-point by applying a time weight of each user based on a departure point between users, and a search module for providing a search for a route to the calculated mid-point. In addition, based on the mid-point search result, it is possible to increase the utilization rate of users by including a place recommending function based on user's preference. Experimental results show that the proposed system can increase the efficiency of using by the user-dependent mid-point navigation and place recommendation function.

• Journal of Korea Multimedia Society
• /
• v.23 no.2
• /
• pp.343-350
• /
• 2020

In the era of the Fourth Industrial Revolution in the digital revolution is taking place, various attempts have been made to provide various contents in a digital environment. In this paper, agent-recruitment service system based on collaborative deep learning is proposed for the intelligent head hunting service. The service system is improved from previous research [7] using collaborative deep learning for more reliable recommendation results. The Collaborative deep learning is a hybrid recommendation algorithm using "Recurrent Neural Network(RNN)" specialized for exponential calculation, "collaborative filtering" which is traditional recommendation filtering methods, and "KNN-Clustering" for similar user analysis. The proposed service system can expect more reliable recommendation results than previous research and showed high satisfaction in user survey for verification.

• The Korean Journal of Community Living Science
• /
• v.22 no.3
• /
• pp.327-339
• /
• 2011

This study aims to investigate relationships between benefit sought and selection attributes of green tea consumers. For data collection, a total of 595 copies of questionnaires were collected by convenience sampling in the Seoul and Gyeonggi-do area. The data were analyzed by using SPSS 15.0. The factor analysis identified four dimensions of the benefit sought : health benefit, sensory, sociality, and self-esteem. Six dimensions of selection attributes were identified as manufacturing, design, sensory appeal, recommendation, utility and brand. The results of the canonical correlation analysis indicated that health benefit, sensory, sociality of benefit sought and manufacturing, design, sensory appeal, recommendation, utility, brand of selection attributes were highly correlated, and the self-esteem of benefit sought and recommendation of selection attribute were highly correlated. This means it is important to place an emphasis on safety production, package design, sensory characteristics, product description, utility and brand for consumers who seek health benefit, flavor and sociality. It is also important to place an emphasis on product description for consumers who pursue self-esteem benefits. Green tea marketers should consider benefit sought aspects as the most important factors affecting selection attributes on green tea purchasing.

• Smart Media Journal
• /
• v.5 no.2
• /
• pp.84-89
• /
• 2016

For a place-recommendation model based on user's behavior and multi-attribute attitude in this thesis. We focus groups that show similar patterns of visiting restaurants and then compare one and the other. We make use of The Fishbein Equation, Pearson's Correlation Coefficient to calculate multi-attribute attitude scores. Furthermore, We also make use of Preference Prediction Algorithm and Distance based method named "Euclidean Distance" to provide accurate results. We can demonstrate how excellent this system is through several experiments carried out with actual data.

• Journal of Information Science Theory and Practice
• /
• v.12 no.2
• /
• pp.64-78
• /
• 2024

Sentiment analysis is one of the promising approaches for developing a point of interest (POI) recommendation system. It uses natural language processing techniques that deploy expert insights from user-generated content such as reviews and feedback. By applying sentiment polarities (positive, negative, or neutral) associated with each POI, the recommendation system can suggest the most suitable POIs for specific users. The proposed study combines two models for POI recommendation. The first model uses bidirectional long short-term memory (BiLSTM) to predict sentiments and is trained on an election dataset. It is observed that the proposed model outperforms existing models in terms of accuracy (99.52%), precision (99.53%), recall (99.51%), and F1-score (99.52%). Then, this model is used on the Foursquare dataset to predict the class labels. Following this, user and POI embeddings are generated. The next model recommends the top POIs and corresponding coordinates to the user using the LSTM model. Filtered user interest and locations are used to recommend POIs from the Foursquare dataset. The results of our proposed model for the POI recommendation system using sentiment analysis are compared to several state-of-the-art approaches and are found quite affirmative regarding recall (48.5%) and precision (85%). The proposed system can be used for trip advice, group recommendations, and interesting place recommendations to specific users.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.39 no.4
• /
• pp.60-66
• /
• 2016

Compound logistics is a service aimed to enhance logistics efficiency by supporting that shippers and consigners jointly use logistics facilities. Many of these services have taken place both domestically and internationally, but the joint logistics services for e-commerce have not been spread yet, since the number of the parcels that the consigners transact business is usually small. As one of meaningful ways to improve utilization of compound logistics, we propose a brokerage service for shipper and consigners based on the hybrid recommendation system using very well-known classification and clustering methods. The existing recommendation system has drawn a relatively low satisfaction as it brought about one-to-one matches between consignors and logistics vendors in that such matching constrains choice range of the users to one-to-one matching each other. However, the implemented hybrid recommendation system based brokerage agent service system can provide multiple choice options to mutual users with descending ranks, which is a result of the recommendation considering transaction preferences of the users. In addition, we applied feature selection methods in order to avoid inducing a meaningless large size recommendation model and reduce a simple model. Finally, we implemented the hybrid recommendation system based brokerage agent service system that shippers and consigners can join, which is the system having capability previously described functions such as feature selection and recommendation. As a result, it turns out that the proposed hybrid recommendation based brokerage service system showed the enhanced efficiency with respect to logistics management, compared to the existing one by reporting two round simulation results.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.5
• /
• pp.1708-1727
• /
• 2021

In the advent of the twenty-first century, human beings began to closely interact with technology. Today, technology is developing, and as a result, the world wide web (www) has a very important place on the Internet and the significant task is fulfilled by Web services. A lot of Web services are available on the Internet and, therefore, it is difficult to find matching Web services among the available Web services. The recommendation systems can help in fixing this problem. In this paper, our observation was based on the recommended method such as the collaborative filtering (CF) technique which faces some failure from the data sparsity and the cold-start problems. To overcome these problems, we first applied an ontology-based clustering and then the k-nearest neighbor (KNN) algorithm for each separate cluster group that effectively increased the data density using the past user interests. Then, user ratings were predicted based on the model-based approach, such as singular value decomposition (SVD) and the predictions used for the recommendation. The evaluation results showed that our proposed approach has a less prediction error rate with high accuracy after analyzing the existing recommendation methods.

• The Korean Journal of Franchise Management
• /
• v.14 no.2
• /
• pp.1-17
• /
• 2023

Purpose: Due to the COVID-19 and inflation, participation sports companies, including fitness centers, are facing challenges. Since a fitness center must simultaneously manage facilities and operate services, both factors must be considered when developing a marketing strategy. Therefore, this study examines the effects of expanded marketing mix factors (price, physical evidence, place, people, product, and promotion) including facilities and services on the consumption behavior (satisfaction, recommendation intention, repurchase intention) of fitness center customers. Research design, data, and methodology: The data were collected from sample of 323 fitness club members in Seoul and analyzed with SPSS Win Ver.28.0 program. Result: The specific results of the study were as follows; First, extended marketing mix factors had significant positive (+) effect on satisfaction. Second, extended marketing mix factors had significant positive (+) effect on recommendation intention. Third, extended marketing mix factors had significant positive (+) effect on repurchase intention. Fourth, satisfaction had significant positive (+) effect on recommendation intention and repurchase intention. Conclusions: To encourage consumption behavior, it is necessary to convert existing customers into loyal ones by increasing satisfaction and establishing a virtuous cycle structure that recommends them to others while also improving repurchase intention.

• Journal of the Korea Convergence Society
• /
• v.11 no.8
• /
• pp.23-31
• /
• 2020

The need for personalized recommendation is growing due to convenient access and various types of items due to the development of information communication and smartphones. Weather and weather conditions have a great influence on the decision-making of users' places and activities. This weather information can increase users' satisfaction with recommendations. In this paper, we propose a collaborative filtering-based place recommendation system using living index by utilizing living index of users' location information on mobile platform to find users with similar propensity and to recommend places by predicting preferences for places. The proposed system consists of a weather module for analyzing and classifying users' weather, a recommendation module using collaborative filtering for place recommendations, and a management module for user preferences and post-management. Experiments have shown that the proposed system is valid in terms of the convergence of collaborative filtering algorithms and living indices and reflecting individual propensity.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.23 no.5
• /
• pp.177-182
• /
• 2023

When determining a meeting place, it is always a cumbersome problem to select an appropriate store with a short travel time for all participants. In this paper, to solve this problem, we propose an algorithm that recommends the best place and store based on the subway station and develop the system. This system provides a web-based store information registration function that allows self-employed people to register and promote their store, and provides an app-based function to recommend a meeting place to participants. The proposed algorithm reduces the travel time of all participants based on the subway map and improves fairness by using the standard deviation of the required time. In addition, this system presents a new way for self-employed people who have recently relied only on publicity through delivery apps.