• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1643-1651
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• 2014

Social networking services (in short, SNS) allow users to share their own data with family, friends, and communities. Since there are many kinds of information that has been uploaded and shared through the SNS, the amount of information on the SNS keeps increasing exponentially. Particularly, Facebook has adopted some interesting features related to entertainment (e.g., movie, music and TV show). However, they do not consider contextual information of users for recommendation (e.g., time, location, and social contexts). Therefore, in this paper, we propose a novel approach for movie recommendation based on the integration of a variety contextual information (i.e., when the users watched the movies, where the users watched the movies, and who watched the movie with them). Thus, we developed a Facebook application (called MyMovieHistory) for recording the movie history of users and recommending relevant movies.

• Journal of Information Technology and Architecture
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• v.9 no.3
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• pp.333-344
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• 2012

This paper proposes a system which recommends movies using information from social network services containing personal interest and taste. Method for establishing data is as follows. The system gathers movies' information from web sites and user's information from social network services such as Facebook and twitter. The data from social network services is categorized into six steps of emotion level for more accurate processing following users' emotional states. Gathered data will be established into vector space model which is ideal for analyzing and deducing the information with the system which is suggested in this paper. The existing similarity measurement method for movie recommendation is presentation of vector information about emotion level and similarity measuring method on the coordinates using Cosine measure. The deducing method suggested in this paper is two-phase arithmetic operation as follows. First, using general cosine measurement, the system establishes movies list. Second, using similarity measurement, system decides recommendable movie list by vector operation from the coordinates. After Comparative Experimental Study on the previous recommendation systems and new one, it turned out the new system from this study is more helpful than existing systems.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.1-18
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• 2016

Collaborative Filtering (CF) predicts the focal user's preference for particular item based on user's preference rating data and recommends items for the similar users by using them. It is a popular technique for the personalization in e-commerce to reduce information overload. However, it has some limitations including sparsity and scalability problems. In this paper, we use a method to integrate social network information into collaborative filtering in order to mitigate the sparsity and scalability problems which are major limitations of typical collaborative filtering and reflect the user's qualitative and emotional information in recommendation process. In this paper, we use a novel recommendation algorithm which is integrated with collaborative filtering by using Social SVD++ algorithm which considers social network information in SVD++, an extension algorithm that can reflect implicit information in singular value decomposition (SVD). In particular, this study will evaluate the performance of the model by reflecting the real-world user's social network information in the recommendation process.

• Proceedings of the Korea Contents Association Conference
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• 2017.05a
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• pp.453-454
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• 2017

The travellers using social media leave their location history in the form of trajectories. These trajectories can be bridged for acquiring information, required for future recommendation for the future travelers, who are new to that location, providing all sort of information. In this paper, we propose a personalized travel path recommendation scheme based on social life log. By taking advantage of two kinds of social media such as travelogue and community contributed photos, the proposed scheme can not only be personalized to user's travel interest but also be able to recommend a travel path rather than individual Points of Interest (POIs). It also maps both user's and routes' textual descriptions to the topical package space to get user topical package model and route topical package model (i.e., topical interest, cost, time and season).

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.112-112
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• 2012

This paper presents a Person Concerned Theme Recommendation System to help users take an active part in realistic social activities.

• Journal of Digital Convergence
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• v.15 no.1
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• pp.453-459
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• 2017

This study was conducted to review the usage behavior of mobile social network games that are attracting attention as a new growth engine in the game market. To that end, a survey was conducted on 250 male and female university students. The result of the study showed that first, the relationship formation motivation and seeking fun during leisure times in association with mobile social network games had a positive effect on game attitudes. Second, the relationship formation motivation had a positive effect on the continuous use intention. Third, the relationship formation motivation and the fun-seeking motivation had a positive effect on word-of-mouth recommendation, while the relationship formation motivation and advertisement recommendation motivation had a positive effect on the intention to recommendation online formats. Fourth, the attitude towards mobile social network games had a positive effect on the continuous use intention. Lastly, the attitude towards mobile social network games had a positive effect on only the intention to recommendation through word-of-mouth. This study is expected to provide useful and basic data for the development of quality game content that will cater to users' needs.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.420-420
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• 2008

• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.169-185
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• 2017

This study proposes a novel recommendation algorithm that reflects the results from trust/distrust network analysis as a solution to enhance prediction accuracy of recommender systems. The recommendation algorithm of our study is based on memory-based collaborative filtering (CF), which is the most popular recommendation algorithm. But, unlike conventional CF, our proposed algorithm considers not only the correlation of the rating patterns between users, but also the results from trust/distrust relationship network analysis (e.g. who are the most trusted/distrusted users?, whom are the target user trust or distrust?) when calculating the similarity between users. To validate the performance of the proposed algorithm, we applied it to a real-world dataset that contained the trust/distrust relationships among users as well as their numeric ratings on movies. As a result, we found that the proposed algorithm outperformed the conventional CF with statistical significance. Also, we found that distrust relationship was more important than trust relationship in measuring similarities between users. This implies that we need to be more careful about negative relationship rather than positive one when tracking and managing social relationships among users.

• KIISE Transactions on Computing Practices
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• v.22 no.10
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• pp.504-512
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• 2016

In this paper, we propose a personalized event recommendation scheme using user's activity analysis and collaborative filtering in social network environments. The proposed scheme predicts un-evaluated attribute values through analysis of user activities, relationships, and collaborative filtering. The proposed scheme also incorporates a user's recent preferences by considering the recent history for the user or context-aware information to precisely grasp the user's preferences. As a result, the proposed scheme can recommend events to users with a high possibility to participate in new events, preventing indiscriminate recommendations. In order to show the superiority of the proposed scheme, we compare it with the existing scheme through performance evaluation.

• Journal of the Korea Society of Computer and Information
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• v.21 no.7
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• pp.67-75
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• 2016

This study aims to examine how international airport visitors perceived value effects on their satisfaction, revisit and recommendation intention. To archive the research goal 288 questionnaires were collected from Incheon international airport and was analyzed a frequency analysis, reliability analysis, exploratory factor analysis and correlation coefficient analysis from SPSS 21, a hypothesis through out confirmatory factor analysis and structural equation modeling from AMOS 7.0. As a result of the analyses, it was found that the models was appropriate in proving the hypotheses on interrelationships among perceived value, satisfaction and revisit & recommendation intention. First, perceived value is factorized as acquisition value, emotion value, monetary value and social value. Second, all factor of perceived value turned out to have affirmative effects on international airport visitors' satisfaction. Third, international airport visitors satisfaction turned out to have affirmative effects on revisit and recommendation intention. Overall, finding of this study enhance the theoretical progress on the experiential concept in international airport and offer important implication for international airport industry.