• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.27-40
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• 2023

With the increase in the spread of smart devices and the impact of COVID-19, the consumption of media contents through smart devices has significantly increased. Along with this trend, the amount of media contents viewed through OTT platforms is increasing, that makes contents recommendations on these platforms more important. Previous contents-based recommendation researches have mostly utilized metadata that describes the characteristics of the contents, with a shortage of researches that utilize the contents' own descriptive metadata. In this paper, various text data including titles and synopses that describe the contents were used to recommend similar contents. KLUE-RoBERTa-large, a Korean language model with excellent performance, was used to train the model on the text data. A dataset of over 20,000 contents metadata including titles, synopses, composite genres, directors, actors, and hash tags information was used as training data. To enter the various text features into the language model, the features were concatenated using special tokens that indicate each feature. The test set was designed to promote the relative and objective nature of the model's similarity classification ability by using the three contents comparison method and applying multiple inspections to label the test set. Genres classification and hash tag classification prediction tasks were used to fine-tune the embeddings for the contents meta text data. As a result, the hash tag classification model showed an accuracy of over 90% based on the similarity test set, which was more than 9% better than the baseline language model. Through hash tag classification training, it was found that the language model's ability to classify similar contents was improved, which demonstrated the value of using a language model for the contents-based filtering.

• Journal of Broadcast Engineering
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• v.14 no.5
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• pp.625-637
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• 2009

With advent of TV environment and increasing of variety of program contents, users are able to experience more various and complex environment for watching TV contents. According to the change of content watching environment, users have to make more efforts to choose his/her interested TV program contents or TV channels than before. Also, the users usually watch the TV program contents with their own regular way. So, in this paper, we suggests personalized TV program schedule recommendation system based on the analyzing users' TV watching history data. And we extract the users' watched program patterns using the sequential pattern mining method. Also, we proposed a new sequential pattern mining which is suitable for TV watching environment and verify our proposed method have better performance than existing sequential pattern mining method in our application area. In the future, we will consider a VoD characteristic for extending to IPTV program schedule recommendation system.

• Journal of Korea Multimedia Society
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• v.20 no.4
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• pp.704-712
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• 2017

Various researches on content sensibility with the development of digital contents are under way. Emotional research on fonts is also underway in various fields. There is a requirement to use the content expressions in the same way as the content, and to use the font emotion and the textual sensibility of the text in harmony. But it is impossible to select a proper font emotion in Korea because each of more than 6,000 fonts has a certain emotion. In this paper, we analysed emotional classification attributes and constructed the Hangul font recommendation system. Also we verified the credibility and validity of the attributes themselves in order to apply to Korea Hangul fonts. After then, we tested whether general users can find a proper font in a commercial font set through this emotional recommendation system. As a result, when users want to express their emotions in sentences more visually, they can get a recommendation of a Hangul font having a desired emotion by utilizing font-based emotion attribute values collected through the crowdsourced method.

• Journal of Digital Contents Society
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• v.19 no.5
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• pp.845-852
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• 2018

This study has developed a preventive launcher from smartphone addiction for the digital generation and the contents recommendation based on machine learning which used multiple and collective intelligence. This could provide convenient digital nurturing experience for the parents who fear their children's over use of digital devices and also suggest individually adaptive digital learning methods that enhance the learning efficiency and pleasurable and safe learning environment for the children. Suggested application is a kind of gamification launcher that protects children from harmful contents and from smartphone addiction with time limit settings. For parents who find difficulty choosing from various kinds of contents and applications for education, this suggested system could provide a learning analytic report based on big data after collecting and analyzing the data of their children's learning and activities and recommend contents necessary for their kids using recommended algorithm by collective intelligence.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.25-44
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• 2013

Accessing movie contents has become easier and increased with the advent of smart TV, IPTV and web services that are able to be used to search and watch movies. In this situation, there are increasing search for preference movie contents of users. However, since the amount of provided movie contents is too large, the user needs more effort and time for searching the movie contents. Hence, there are a lot of researches for recommendations of personalized item through analysis and clustering of the user preferences and user profiles. In this study, we propose recommendation system which uses ontology based knowledge base. Our ontology can represent not only relations between metadata of movies but also relations between metadata and profile of user. The relation of each metadata can show similarity between movies. In order to build, the knowledge base our ontology model is considered two aspects which are the movie metadata model and the user model. On the part of build the movie metadata model based on ontology, we decide main metadata that are genre, actor/actress, keywords and synopsis. Those affect that users choose the interested movie. And there are demographic information of user and relation between user and movie metadata in user model. In our model, movie ontology model consists of seven concepts (Movie, Genre, Keywords, Synopsis Keywords, Character, and Person), eight attributes (title, rating, limit, description, character name, character description, person job, person name) and ten relations between concepts. For our knowledge base, we input individual data of 14,374 movies for each concept in contents ontology model. This movie metadata knowledge base is used to search the movie that is related to interesting metadata of user. And it can search the similar movie through relations between concepts. We also propose the architecture for movie recommendation. The proposed architecture consists of four components. The first component search candidate movies based the demographic information of the user. In this component, we decide the group of users according to demographic information to recommend the movie for each group and define the rule to decide the group of users. We generate the query that be used to search the candidate movie for recommendation in this component. The second component search candidate movies based user preference. When users choose the movie, users consider metadata such as genre, actor/actress, synopsis, keywords. Users input their preference and then in this component, system search the movie based on users preferences. The proposed system can search the similar movie through relation between concepts, unlike existing movie recommendation systems. Each metadata of recommended candidate movies have weight that will be used for deciding recommendation order. The third component the merges results of first component and second component. In this step, we calculate the weight of movies using the weight value of metadata for each movie. Then we sort movies order by the weight value. The fourth component analyzes result of third component, and then it decides level of the contribution of metadata. And we apply contribution weight to metadata. Finally, we use the result of this step as recommendation for users. We test the usability of the proposed scheme by using web application. We implement that web application for experimental process by using JSP, Java Script and prot$\acute{e}$g$\acute{e}$ API. In our experiment, we collect results of 20 men and woman, ranging in age from 20 to 29. And we use 7,418 movies with rating that is not fewer than 7.0. In order to experiment, we provide Top-5, Top-10 and Top-20 recommended movies to user, and then users choose interested movies. The result of experiment is that average number of to choose interested movie are 2.1 in Top-5, 3.35 in Top-10, 6.35 in Top-20. It is better than results that are yielded by for each metadata.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.277-284
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• 2017

In a flood of information, most users will want to get a proper recommendation. If a recommender system fails to give appropriate contents, then quality of experience (QoE) will be drastically decreased. In this paper, we propose a recommender system based on the intra-cluster users' item preference for improving recommendation accuracy indices such as precision, recall, and F1 score. To this end, first, users are divided into several clusters based on the actual rating data and Pearson correlation coefficient (PCC). Afterwards, we give each item an advantage/disadvantage according to the preference tendency by users within the same cluster. Specifically, an item will be received an advantage/disadvantage when the item which has been averagely rated by other users within the same cluster is above/below a predefined threshold. The proposed algorithm shows a statistically significant performance improvement over the item-based collaborative filtering algorithm with no clustering in terms of recommendation accuracy indices such as precision, recall, and F1 score.

• Journal of Intelligence and Information Systems
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• v.10 no.3
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• pp.133-146
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• 2004

By the development of the network technology, the types and amount of information that users keep in contact with have been dramatically increased. As a result, users are consuming a lot of time and energy to find needed information. On this, this article presents a new methodology that can efficiently manage their information within small cost by using content-based recommendation method and keyword affinity method. By using keyword affinity method, this methodology solves the content-based recommendation method's weak point that the performance is not good within the environment that the preferences of users are rapidly changing and new contents are created continuously and the accuracy level is low until the information of preferences are sufficiently gathered. This article carried out research on the personal e-mail environment where new information is frequently created and disappeared. Also this article assists folder recommendation for the efficient management of e-mail and verified the methodology mentioned above by an experiment to compare the performance of existing folder recommendation methods with the performance of this new method.

• Journal of the Korea Society of Computer and Information
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• v.16 no.7
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• pp.127-135
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• 2011

It is required that an e-learning system has a content recommendation component which helps a learner choose an item. In order to predict items concerning learner's interest, collaborative filtering and content-based filtering methods have been most widely used. The methods recommend items for a learner based on other learner's interests without considering the knowledge level of the learner. So, the effectiveness of the recommendation can be reduced when the number of overall users are relatively small. Also, it is not easy to recommend a newly added item. In order to address the problem, we propose a content recommendation method based on the similarity and the difficulty of an item. By using a recommendation function that reflects both characteristics of items, a higher-level leaner can choose more difficult but less similar items, while a lower-level learner can select less difficult but more similar items, Thus, a learner can be presented items according to his or her level of achievement, which is irrelevant to other learner's interest.

• Journal of Digital Contents Society
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• v.14 no.4
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• pp.419-428
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• 2013

Real-time accessibility and agility are required in u-commerce under ubiquitous computing environment. Most of the existing recommendation techniques adopt the method of evaluation based on personal profile, which has been identified with difficulties in accurately analyzing the customers' level of interest and tendencies, as well as the problems of cost, consequently leaving customers unsatisfied. Researches have been conducted to improve the accuracy of information such as the level of interest and tendencies of the customers. However, the problem lies not in the preconstructed database, but in generating new and diverse profiles that are used for the evaluation of the existing data. Also it is difficult to use the unique recommendation method with hierarchy of each customer who has various characteristics in the existing recommendation techniques. Accordingly, this dissertation used the implicit method without onerous question and answer to the users based on the data from purchasing, unlike the other evaluation techniques. We applied FRAT technique which can analyze the tendency of the various personalization and the exact customer.

• Asia pacific journal of information systems
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• v.15 no.3
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• pp.223-241
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• 2005

In spite of the rapid growth of mobile multimedia contents market, most of the customers experience inconvenience, lengthy search processes and frustration in searching for the specific multimedia contents they want. These difficulties are attributable to the current mobile Internet service method based on inefficient sequential search. To overcome these difficulties, this paper proposes a MOBIIe COntents Recommender System for Movie(MOBICORS-Movie), which is designed to reduce customers' search efforts in finding desired movies on the mobile Internet. MOBICORS-Movie consists of three agents: CF(Collaborative Filtering), CBIR(Content-Based Information Retrieval) and RF(Relevance Feedback). These agents collaborate each other to support a customer in finding a desired movie by generating personalized recommendations of movies. To verify the performance of MOBICORS-Movie, the simulation-based experiments were conducted. The results from this experiments show that MOBICORS-Movie significantly reduces the customer's search effort and can be a realistic solution for movie recommendation in the mobile Internet environment.