• Journal of Digital Convergence
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• v.19 no.11
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• pp.17-22
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• 2021

In this paper, a machine learning-based automatic infographic recommendation system is proposed to improve the existing infographic production method. This system consists of a part that machine learning multiple infographic images and a part that automatically recommends infographics with artificial intelligence only by inputting basic data from the user. The recommended infographics are provided in the form of a library, and additional data can be input by drag & drop method. In addition, the infographic image is designed to be dynamically adjusted according to the size of the input data. As a result of analyzing the machine learning-based automatic infographic recommendation process, the matching success rate for layout and keyword was very high, and the matching success rate for type was rather low. In the future, a study to improve the matching success rate for the image type for each part of the infographic will be needed.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.138-147
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• 2022

Recently, chatbots have been applied in various fields and have shown good results, and many attempts to use chatbots in shopping mall product recommendation services are being conducted on e-commerce platforms. In this paper, for a conversation system that recommends a fashion that a user wants based on conversation between the user and the system and fashion image information, a transformer model that is currently performing well in various AI fields such as natural language processing, voice recognition, and image recognition. We propose a multimodal-based improved transformer model that is improved to increase the accuracy of recommendation by using dialogue (text) and fashion (image) information together for data preprocessing and data representation. We also propose a method to improve accuracy through data improvement by analyzing the data. The proposed system has a recommendation accuracy score of 0.6563 WKT (Weighted Kendall's tau), which significantly improved the existing system's 0.3372 WKT by 0.3191 WKT or more.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.189-194
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• 2010

In the recommendation system that recommends services to a specific user by using the estimation value of other users for users' recommendation service, collaborative filtering methods are widely used. But such recommendation systems have problems that exact classification is not possible because a specific user is classified to already classified group in the course of clustering and inexact result can be recommended in case of big errors in users' estimation values. In this paper, in order to increase estimation accuracy, the researchers suggest a recommendation system that applies collaborative filtering after reclassifying on the basis of a specific user's classification items and then finding and correcting the estimation values of the users beyond the critical value of time. This system uses a method where a specific user is not classified to already classified group in the course of clustering but a group is reorganized on the basis of the specific user. In addition, the researchers correct estimation information by cutting off the subordinate 10% from the trimmed mean of samples and then applies weight over time to the remaining data. As the result of an experiment, the suggested method demonstrated about 14.9%'s more accurate estimation result in case of using MAE than general collaborative filtering method.

• Information Systems Review
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• v.23 no.1
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• pp.155-172
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• 2021

With the recent increase in online product purchases, a recommender system that recommends products considering users' preferences has still been studied. The recommender system provides personalized product recommendation services to users. Collaborative Filtering (CF) using user ratings on products is one of the most widely used recommendation algorithms. During CF, the item-based method identifies the user's product by using ratings left on the product purchased by the user and obtains the similarity between the purchased product and the unpurchased product. CF takes a lot of time to calculate the similarity between products. In particular, it takes more time when using text-based big data such as review data of Amazon store. This paper suggests a hybrid recommendation system using a 2-phase methodology and text data mining to calculate the similarity between products easily and quickly. To this end, we collected about 980,000 online consumer ratings and review data from the online commerce store, Amazon Kinder Store. As a result of several experiments, it was confirmed that the suggested hybrid recommendation system reflecting the user's rating and review data has resulted in similar recommendation time, but higher accuracy compared to the CF-based benchmark recommender systems. Therefore, the suggested system is expected to increase the user's satisfaction and increase its sales.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.145-165
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• 2023

Recommendation system plays a significant role on relieving difficulties of selecting information among rapidly increasing amount of information caused by the development of the Internet and on efficiently displaying information that fits individual personal interest. In particular, without the help of recommendation system, E-commerce and OTT companies cannot overcome the long-tail phenomenon, a phenomenon in which only popular products are consumed, as the number of products and contents are rapidly increasing. Therefore, the research on recommendation systems is being actively conducted to overcome the phenomenon and to provide information or contents that are aligned with users' individual interests, in order to induce customers to consume various products or contents. Usually, collaborative filtering which utilizes users' historical behavioral data shows better performance than contents-based filtering which utilizes users' preferred contents. However, collaborative filtering can suffer from cold-start problem which occurs when there is lack of users' historical behavioral data. In this paper, hybrid music recommendation system, which can solve cold-start problem, is proposed based on the playlist data of Melon music streaming service that is given by Kakao Arena for music playlist continuation competition. The goal of this research is to use music tracks, that are included in the playlists, and metadata of music tracks and playlists in order to predict other music tracks when the half or whole of the tracks are masked. Therefore, two different recommendation procedures were conducted depending on the two different situations. When music tracks are included in the playlist, LightFM is used in order to utilize the music track list of the playlists and metadata of each music tracks. Then, the result of Item2Vec model, which uses vector embeddings of music tracks, tags and titles for recommendation, is combined with the result of LightFM model to create final recommendation list. When there are no music tracks available in the playlists but only playlists' tags and titles are available, recommendation was made by finding similar playlists based on playlists vectors which was made by the aggregation of FastText pre-trained embedding vectors of tags and titles of each playlists. As a result, not only cold-start problem can be resolved, but also achieved better performance than ALS, BPR and Item2Vec by using the metadata of both music tracks and playlists. In addition, it was found that the LightFM model, which uses only artist information as an item feature, shows the best performance compared to other LightFM models which use other item features of music tracks.

• Journal of the Korea Convergence Society
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• v.5 no.4
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• pp.147-153
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• 2014

To create a personal menu, there are a number of considerations. Personal menus are different depending on the dietary therapy for disease, diet for weight control. In addition, the menu you choose, depending on the personal preference and the season, the weather, current personal feelings may differ. An individual should expect to recommend a balanced diet, taking nutritional status just for health care. In this paper, we propose a personalized menu recommendations System framework to meet such needs. To recommend menus the system receives data of the body's individual circumstances, ingredients situation, environmental conditions, psychological condition, emotional condition and provides a recommended menu by performing the inference using the ontology generated from external application systems. In order to provide such services, Internet of Things (IoT) environment should be the foundation. In this paper, we propose a personalized diet recommendation system framework in the IoT standardization environment that has oneM2M common service platform.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1129-1143
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• 2009

This paper proposes a recommendation system (Association Rule System for Targeting) which considers target which is not considered by previous Logistic Regression system, and proves that the efficiency of the recommendation system is better than that of the current and previous Apriori algorithm system. Also this study shows that the click and purchasing rate of the proposed Association Rule System for Targeting is much higher than those of current Apriori algorithm system after the purchasing campaign even though the open rate of the former is lower than that of the latter. In comparison with Logistic Regression methodology, this paper proves with experimental data that the purchasing effect of the proposed system for specific items is much higher in accuracy than that of current Apriori algorithm system even though the purchasing rate of current Apriori algorithm system is higher in whole shopping malls than that of the proposed Association Rule System for Targeting.

• The Journal of the Korea Contents Association
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• v.9 no.5
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• pp.346-354
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• 2009

This paper proposes a new learning recommendation system for learning patterns that educators are applying to learners using item-based method. The proposed method in this paper first collects personal learning methods based on learning information that learners are performing through the internet contents site. Then this system recommends a learning method which is estimated most properly to learners after classifying learning elements based on these information. The students of a middle school took part in the experiment in order to evaluate the proposed system, and the students were divided into three groups according to their grades. We gave inter-attribute and intra-attribute weights to learning elements applying to each group for recommending the most efficient method to improve learning achievement. The experiment showed that the learning achievement of learners in the proposed method is improved considerably compared to the previous grades.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.1
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• pp.1-6
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• 2013

Variety of smart devices including smart phone have become and essential item in user's daily life. This means that smart devices are good mediators to get collecting user's behavior by sensors mounted on the devices. The information from smart devices is important clues to identify by analyzing the user's preferences and needs. Through this, the intelligent service which is fitted to the user is possible. This paper propose a smart service recommendation model based on user scenario using fusion context-awareness. The information for recommendation services is collected to make the scenario depending on time, location, action based on the Fusion process. The scenarios can help predict a user's situation and provide the services in advance. Also, content categories as well as the content types are determined depending on the scenario. The scenario is a method for providing the best service as well as a basis for the user's situation. Using this method, proposing a smart service model with the fusion context-awareness based on the hybrid sensing is the goal of this paper.

• 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.