• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.109-114
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• 2018

Collaborative filtering has been widely utilized in recommender systems as typical algorithm for outstanding performance. Since it depends on item rating history structurally, The more sparse rating matrix is, the lower its recommendation accuracy is, and sometimes it is totally useless. Variety of hybrid approaches have tried to combine collaborative filtering and content-based method for improving the sparsity issue in rating matrix. In this study, a new method is suggested for the same purpose, but with different perspective, it deals with no-match situation in person-person similarity evaluation. This method is called the transitive similarity model because it is based on relation graph of people, and it compares recommendation accuracy by applying to Movielens open dataset.

• Management & Information Systems Review
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• v.36 no.4
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• pp.129-148
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• 2017

Conventional exhibition management information systems recommend tourist attractions that are close to the place in which an exhibition is held. Some recommended attractions by the location-based recommendation could be meaningless when nothing is related to the exhibition's topic. Our goal is to recommend attractions that are related to the content presented in the exhibition, which can be coined as content-based recommendation. Even though human exhibition curators can do this, the quality is limited to their manual task and knowledge. We propose an automatic way of discovering attractions relevant to an exhibition of interests. Language resources are incorporated to discover attractions that are more meaningful. Because a typical single machine is unable to deal with such large-scale language resources efficiently, we implemented the algorithm on top of Apache Spark, which is a well-known distributed computing framework. As a user interface prototype, a web-based system is implemented that provides users with a list of relevant attractions when users are browsing exhibition information, available at http://bike.snu.ac.kr/WARP. We carried out a case study based on Korean Tourism Organization Linked Open Data with Korean Wikipedia as a language resource. Experimental results are demonstrated to show the efficiency and effectiveness of the proposed system. The effectiveness was evaluated against well-known exhibitions. It is expected that the proposed approach will contribute to the development of both exhibition and tourist industries by motivating exhibition visitors to become active tourists.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.15-20
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• 2023

This paper is a study on the development of an artificial intelligence (AI) system algorithm that recommends indirect advertising products suitable for character web dramas. The goal of this study is to increase viewers' content immersion and help them understand the story of the drama more deeply by recommending indirect advertising products that are suitable for writing lines for web dramas. In this study, we analyze dialogue and plot using the natural language processing model GPT, and develop two types of indirect advertising product recommendation systems, including prop type and background type, based on the analysis results. Through this, products that fit the story of the web drama are appropriately placed, allowing indirect advertisements to be exposed naturally, thereby increasing viewer immersion and enhancing the effectiveness of product promotion. There are limitations of artificial intelligence models, such as the difficulty in fully understanding hidden meanings or cultural nuances, and the difficulty in securing sufficient data for learning. However, this study will provide new insights into how AI can contribute to the production of creative works, and will be an important stepping stone to expand the possibilities of using natural language processing models in the creative industry.

• Journal of Broadcast Engineering
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• v.17 no.1
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• pp.129-139
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• 2012

This paper introduces a recommender system that is to recommend broadcast content. Our recommender system uses user's viewing history for personalized recommendations. Broadcast contents has unique characteristics as compared with books, musics and movies. There are two types of broadcast content, a series program and an episode program. The series program is comprised of several programs that deal with the same topic or story. Meanwhile, the episode program covers a variety of topics. Each program of those has different topic in general. Therefore, our recommender system recommends TV programs to users according to the type of broadcast content. The recommendations in this system are based on user's viewing history that is used to calculate content similarity between contents. Content similarity is calculated by exploiting collaborative filtering algorithm. Our recommender system uses java sparse array structure and performs memory-based processing. And then the results of processing are stored as an index structure. Our recommender system provides recommendation items through OPEN APIs that utilize the HTTP Protocol. Finally, this paper introduces the implementation of our recommender system and our web demo.

• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.201-216
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• 2008

Nowadays various new items are available, but limitation of searching effort makes it difficult for customers to search new items which they want to purchase. Therefore new item providers and customers need recommendation systems which recommend right items for right customers. In this research, we focus on the new item recommendation issue, and suggest preference boundary- based procedures which extend traditional content-based algorithm. We introduce the concept of preference boundary in a feature space to recommend new items. To find the preference boundary of a target customer, we suggest heuristic algorithms to find the centroid and the radius of preference boundary. To evaluate the performance of suggested procedures, we have conducted several experiments using real mobile transaction data and analyzed their results. Some discussions about our experimental results are also given with a further research area.

• The KIPS Transactions:PartD
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• v.10D no.2
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• pp.309-316
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• 2003

In this paper, we propose a new watermarking method based on spread spectrum and a semi-fingerprinting system architecture that can be built using our robust watermarking method. Especially, we describe a method that extends the application area of watermarking technology to more practical application domains by applying the watermarking technology that has been focused mainly on copyright protection to fingerprinting area. Our proposed watermarking scheme uses the method that inserts more data by using random number shifting method. We improved the reliability of acquired data with 20-bits CRC code and 60-bits inserted information. In addition, we designed the system architecture based on the recommendation of cIDf (content ID forum) in order to apply the system on the semi-fingerprinting area.

• Journal of Distribution Science
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• v.19 no.1
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• pp.89-103
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• 2021

Purpose: This study is to explore how Netflix Customers' Engagement was influenced by Instagram Content, Perceived Price, Exclusivity, and Motivation in the context of Media Streaming and the role of Willingness to Subscribe as the mediating variable. This study underlines millennial's willingness to engage and the form of engagement. Research design, data, and methodology: The data for this research were collected from 100 Netflix's Millennials subscribers who follow @netflixid Instagram. All the results were analyzed and verified using SEM-PLS. Results: Research findings indicated that Willingness to Subscribe, Exclusivity, Motivation, and Instagram Content positively influenced Customer Engagement among Netflix millennials' subscribers. In contrast, Perceived Price had a negative effect on Customer Engagement. Conclusions: As a consequence, the exclusivity that Netflix offers to its audience by a recommendation algorithm has been proven to increase the engagement. This study also disclosed that the most definite form of positive engagement shown by Netflix millennials' subscribers is a behavioral aspect, where they positively recommend Netflix (word of mouth). The study findings can be a reference for the media streaming industry in their efforts to strengthen the engagement with their customers, especially the millennials, and provide knowledge about consumer behavior in digital technology.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.521-528
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• 2021

Existing image analysis systems in use in the military field are carried out by readers analyzing and identifying images themselves, writing and disseminating related content, and in this process, repetitive tasks are frequent, resulting in workload. In this paper, to solve the previous problem, we proposed an algorithm that can operate the Seq2Seq model on a word basis, which operates on a sentence basis, and applied the Attention technique to improve accuracy. In addition, by applying the Beam Search technique, we would like to recommend various current identification sentences based on the past identification contents of a specific area. It was confirmed through experiments that the Beam Search technique recommends sentences more effectively than the existing greedy Search technique, and confirmed that the accuracy of recommendation increases when the size of Beam is large.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.12
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• pp.1911-1916
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• 2013

One significant challenge that arises in social tagging systems is the rapid increase in the number and diversity of the tags. As opposed to structured annotation systems, tags provide users an unstructured, open-ended mechanism to annotate and organize web-content. In this paper, we propose a scheme for URL recommendation that is based on a folksonomy which is comprised of user-defined tags, URL-keywords and the category folder name as the major element. This scheme will be further improved and implemented on a browser extension that recommends to users the best way to classify a particular URL.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.