• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.4
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• pp.953-960
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• 2016

Due to the prevalent use of online systems and the increasing amount of accessible information, the influence of recommender systems is growing bigger than ever. However, there are several attempts by malicious users who try to compromise or manipulate the reliability of recommender systems with cyber-attacks. By analyzing the ratio of 'sympathy' against 'apathy' responses about a concerned review and reflecting the results in a recommendation system, we could present a way to improve the performance of a recommender system and maintain a robust system. After collecting and applying actual movie review data, we found that our proposed recommender system showed an improved performance compared to the existing recommendation systems.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.185-198
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• 2021

With the development of Internet technology, because traditional recommendation algorithms cannot learn the in-depth characteristics of users or items, this paper proposed a recommendation algorithm based on the AMITI(attention mechanism and improved TF-IDF) to solve this problem. By introducing the two-layer attention mechanism into the CNN, the feature extraction ability of the CNN is improved, and different preference weights are assigned to item features, recommendations that are more in line with user preferences are achieved. When recommending items to target users, the scoring data and item type data are combined with TF-IDF to complete the grouping of the recommendation results. In this paper, the experimental results on the MovieLens-1M data set show that the AMITI algorithm improves the accuracy of recommendation to a certain extent and enhances the orderliness and selectivity of presentation methods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.131-137
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• 2018

People write reviews of numerous products or services on the Internet, in their blogs or community bulletin boards. These unstructured data contain important emotions and opinions about the author's product or service, which can provide important information for future product design or marketing. However, this text-based information cannot be evaluated quantitatively, and thus they are difficult to apply to mathematical models or optimization problems for product design and improvement. Therefore, this study proposes a method to quantitatively extract user's opinion or preference about a specific product or service by utilizing a lot of text-based information existing on the Internet or online. The extracted unstructured text information is decomposed into basic unit words, and positive rate is evaluated by using existing emotional dictionaries and additional lists proposed in this study. This can be a way to effectively utilize unstructured text data, which is being generated and stored in vast quantities, in product or service design. Finally, to verify the effectiveness of the proposed method, a case study was conducted using movie review data retrieved from a portal website. By comparing the positive rates calculated by the proposed framework with user ratings for movies, a guideline on text mining based evaluation of unstructured data is provided.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.156-163
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• 2006

CF systems are widely used in recommendation due to the easy implementation and the outstanding performance. They have several problems such as the sparsity problem, the first-rater problem, and recommending explanation. Many studies are suggested to resolve these problems. While the influence of the sparsity problem lessens as the users' data are accumulated, but the first-rater problem is originated from the CF systems and there are a number of researches to overcome the disadvantages of CF systems based on the content-based methods. Also CF systems are black boxes, providing no explanation of working of the recommendation. In this paper we present a content-based prediction system based on the preference words, which exposes the reasoning behind a recommendation. Our system predicts user's rating of a new movie and we suggest a semiotic network-based method to solve the mismatching problem between the items. For experimental comparison, we used EachMovie and IMDb dataset.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.201-207
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• 2022

In this paper, we propose a movie recommendation model that uses the users' ratings as well as their reviews. To understand the user's preference from multicriteria perspectives, the proposed model is designed to apply attribute-based sentiment analysis to the reviews. For doing this, it divides the reviews left by customers into multicriteria components according to its implicit attributes, and applies BERT-based sentiment analysis to each of them. After that, our model selectively combines the attributes that each user considers important to CF to generate recommendation results. To validate usefulness of the proposed model, we applied it to the real-world movie recommendation case. Experimental results showed that the accuracy of the proposed model was improved compared to the traditional CF. This study has academic and practical significance since it presents a new approach to select and use models in consideration of individual characteristics, and to derive various attributes from a review instead of evaluating each of them.

• Korean Journal of Cognitive Science
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• v.25 no.2
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• pp.135-157
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• 2014

The present study investigated whether 6- and 12-month-old infants could infer an agent's social preference on the basis of intentions. In Experiment 1, 12-month-old infants were first familiarized with two kinds of event: the helping and the hindering events. In the helping event, an agent (either a square or triangle) tried to help a circle climb up the hill and the movie stopped right before the circle reached the top of the hill. Thus, the outcome of the helping behavior was made to be ambiguous. Similarly, in the hindering movie, another agent tried to hinder the circle from reaching the top of the hill and the movie stopped right before the circle slipped down to the base of the hill making the final outcome of the hindering behavior unclear. During the test trial, infants were either presented with an event in which the circle approached the helper (approach-helper condition) or an event in which the circle approached the hinderer (approach-hinderer condition). The results indicated that both 6- and 12-month-olds looked longer at the approach-helper event than at the approach-hinderer event. Thus, by 6 months of age, infants are sensitive to agents' intentions when reasoning about agents' social preference. The current findings add to the emerging evidence on social evaluation and moral reasoning during infancy.

• Journal of Science Education
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• v.40 no.1
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• pp.31-51
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• 2016

The purpose of this study was to develop "Movie Production Project" Science-Arts convergence STEAM program for elementary students and investigate the effects of the program on career orientation of science, career awareness and creative personality. Participants were 82 elementary school students. The results of this study were as follows: First, experimental group's total score of career orientation of science was significantly higher than that of comparative group. Among sub-areas, science learning preference, science career preference and perception on science career worth were significantly different between two groups. Experimental group's scores were significantly higher than those of comparative group. Second, experimental group's career awareness total score was significantly higher than that of comparative group including all sub-areas. Third, experimental group's creative personality total score was significantly higher than that of comparative group including 2 sub-areas, independence, openness. Finally, experimental group student's perception on the program showed that it was interesting, a little easy and they hoped to study again.

• Korean journal of communication and information
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• v.51
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• pp.47-64
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• 2010

Korea film market is heterogeneously divided market that comes from competition between Korean movies and foreign imported movies. This research empirically analyzes genre differentiation in Korean film market with three dimensions (film audience preference, production and import, box office hit). The results indicate that, first, audiences who preferring Korean movie preferred 'cultural factor oriented genres', but audiences who preferring foreign movie preferred 'high budget oriented genres'. Second, imported foreign movie genre distribution was little bit different with box office hit genre of foreign movies. Foreign movie was imported not only hit genre (action genre) but also low cost genre (comedy and Drama/melodrama genre), but most of all Korean film was produced in box office hit genres (comedy and Drama/melodrama genre), third, Korean movies hit a box office in comedy and Drama/melodrama genre, but foreign movies hit a box office in action and SF/Fantasy genre. Those results show that Korea movies' genres are concentrated very much in cultural factor oriented genre. Those results can give implication of diversity policy and movie production strategy of Korea film market.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.2
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• pp.301-314
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• 2016

Juels and Sudan's fuzzy vault scheme has been applied to various researches due to its error-tolerance property. However, the fuzzy vault scheme does not consider the difference between people's preferences, even though the authors instantiated movie lover' case in their paper. On the other hand, to make secure and high performance face authentication system, Nyang and Lee introduced a face authentication system, so-called fuzzy face vault, that has a specially designed association structure between face features and ordinary fuzzy vault in order to let each face feature have different weight. However, because of optimizing intra/inter class difference of underlying feature extraction methods, we can easily expect that the face authentication system does not successfully decrease the face authentication failure. In this paper, for ensuring the flexible use of the fuzzy vault scheme, we introduce the bucket structure, which differently implements the weighting idea of Nyang and Lee's face authentication system, and three distribution functions, which formalize the relation between user's weight of preferences and system implementation. In addition, we suggest a matchmaker scheme based on them and confirm its computational performance through the movie database.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.316-322
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• 2014

A movie recommendation system is proposed to learn a preference model of a viewer by using multimodal features of a video content and their evoked implicit responses of the viewer in synchronized manner. In this system, facial expression, body posture, and physiological signals are measured to estimate the affective states of the viewer, in accordance with the stimuli consisting of low-level and affective features from video, audio, and text streams. Experimental results show that it is possible to predict arousal response, which is measured by electrodermal activity, of a viewer from auditory and text features in a video stimuli, for estimating interestingness on the video.