• Title/Summary/Keyword: media recommendation

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A Study on Story propose model based on Machine Learning - Focused on YouTube

  • CHUN, Sanghun;SHIN, Seung-Jung
    • International Journal of Internet, Broadcasting and Communication
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    • v.13 no.2
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    • pp.224-230
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    • 2021
  • YouTube is an OTT service that leads the home economy, which has emerged from the 2020 Corona Pandemic. With the growth of OTT-based individual media, creators are required to establish attractive storytelling strategies that can be preferred by viewers and elected for YouTube recommendation algorithms. In this study, we conducted a study on modeling that proposes a content storyline for creators. As the ability for Creators to create content that viewers prefer, we have presented the data literacy ability to find patterns in complex and massive data. We also studied the importance of compelling storytelling configurations that viewers prefer and can be selected for YouTube recommendation algorithms. This study is of great significance in that it deviated from the viewer-oriented recommendation system method and proposed a story suggestion model for individual creaters. As a result of incorporating this story proposal model into the production of the YouTube channel Tiger Love video, it showed a certain effectiveness. This story suggestion model is a machine learning text-based story suggestion system, excluding the application of photography or video.

LSTM-based IPTV Content Recommendation using Watching Time Information (시청 시간대 정보를 활용한 LSTM 기반 IPTV 콘텐츠 추천)

  • Pyo, Shinjee;Jeong, Jin-Hwan;Song, Injun
    • Journal of Broadcast Engineering
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    • v.24 no.6
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    • pp.1013-1023
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    • 2019
  • In content consumption environment with various live TV channels, VoD contents and web contents, recommendation service is now a necessity, not an option. Currently, various kinds of recommendation services are provided in the OTT service or the IPTV service, such as recommending popular contents or recommending related contents which similar to the content watched by the user. However, in the case of a content viewing environment through TV or IPTV which shares one TV and a TV set-top box, it is difficult to recommend proper content to a specific user because one or more usage histories are accumulated in one subscription information. To solve this problem, this paper interprets the concept of family as {user, time}, extends the existing recommendation relationship defined as {user, content} to {user, time, content} and proposes a method based on deep learning algorithm. Through the proposed method, we evaluate the recommendation performance qualitatively and quantitatively, and verify that our proposed model is improved in recommendation accuracy compared with the conventional method.

Personalized University Educational Contents Recommendation Scheme for Job Curation Systems (취업 큐레이션 시스템을 위한 개인 맞춤형 교육 콘텐츠 추천 기법)

  • Lim, Jongtae;Oh, Youngho;Choi, JaeYong;Pyun, DoWoong;Lee, Somin;Shin, Bokyoung;Chae, Daesung;Bok, Kyoungsoo;Yoo, Jaesoo
    • The Journal of the Korea Contents Association
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    • v.21 no.7
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    • pp.134-143
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    • 2021
  • Recently, with the development of mobile devices and social media services, contents recommendation schemes have been studied. They are typically applied to the job curation systems. Most existing university education content recommendation schemes only recommend the most frequently taken subjects based on the student's school and major. Therefore, they do not consider the type or field of employment that each student wants. In this paper, we propose a university educational contents recommendation scheme for job curation services. The proposed scheme extracts companies that a user is interested in by analyzing his/her activities in the job curation system. The proposed scheme selects graduates or mentors based on the reliability and similarity of graduates who have been employed at the companies of interest. The proposed scheme recommends customized subjects, comparative subjects, and autonomous activity lists to users through collaborative filtering.

A Study on Contents Preference Prediction Method using Tags based on Content-based Filtering (Tag를 이용한 CBF방식의 컨텐츠 선호도 예측 방법)

  • Um, Tae-Kwang;Choi, Sung-Hwan;Lee, Jae-Hwang
    • Proceedings of the IEEK Conference
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    • 2008.06a
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    • pp.613-614
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    • 2008
  • A content recommendation according to users preferences comes up in the Internet application due to contents overwhelming. This paper newly proposes a method to predict contents preference using tags in conjunction with Content-Based Filtering. By implementing this method, this paper cleans up the contents sparsity problem in Content-Based Filtering, and shows the outstanding improvements.

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Producdt Recommendation System based on User Purchase Priority (사용자 구매 우선순위를 반영한 상품 추천 시스템)

  • Hwang, Doyeun;Kim, Jihan;Kim, Jongwan;Kim, Hankil;Jung, Hoekyung
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2019.05a
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    • pp.502-503
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    • 2019
  • In the existing system that recommends through review data analysis, it does not reflect personal preference details such as user's characteristics or product purchase tastes, in this paper, we propose a system that provides customized recommendation information to various users by selecting the criterion that the user thinks most importantly when searching for the product and purchasing the product, and analyzing it. This is because the user's personal preference is reflected by arranging the product list based on the criterion that the user occupies the largest portion of the product purchase, so that it is more efficient than the recommendation through the recommendation system.

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Analysis Product Recommendation Service Using Image-Based AI Skin Color Detecting Technology (이미지 기반 AI 피부 컬러 측정 기술 및 서비스 적용에 관한 고찰)

  • Park, Hakgwon;Lim, Young-Hwan;Lin, Bin
    • The Journal of the Convergence on Culture Technology
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    • v.8 no.3
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    • pp.501-506
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    • 2022
  • The prolonged of the Post Corona, many Cosmetic company launched various online services. In this paper, consider about the quality of product recommendation using personal color detecting technology. Using the detecting tool which is most widely used by cosmetic company. we will do a lot of testing with this tool and also testing with color detecting equipment. For precise experimental results, it was conducted in a consistent experimental environment. This experiment can be a foundation that can be well used for the expansion of personalized product recommendation services according to the current image-based skin color measurement.

Implementation of Recommender System of Seoul Urban Parks Using Rule-based Expert System based on PROLOG (PROLOG기반의 규칙 기반 전문가 시스템을 이용한 서울시 도시 공원 추천 시스템 구현)

  • Son, Se-Jin;Kim, Da-Hee;Cho, Ye-Bon;Chun, Soo-Wan;Lee, Kang-Hee
    • Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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    • v.7 no.7
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    • pp.847-856
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    • 2017
  • In this paper, we propose a system to users which recommends suitable park using linguistic objects by rule-based inference engine which is made with Prolog. According to the function of city park, which provides positive elements to people such as social, psychological, environmental, and physical, Seoul city park is classified into 6 categories. The classified parks are recommended to users based on the rule based expert system. Rule-based object of park recommendation designs nine linguistic objects based on activity, multi-purposiveness, accessibility, and usage of time. This assigns allowed value accordingly. Generated rules by using these values are fired by user's preference, and infer recommended park. Information on preferences is obtained by way of dialogue, in which the user is asked questions about the three elements that are the criteria for choosing a park. As a result, through the park recommendation system, we intend to increase the user's satisfaction of using park and leisure activities.

Analysis on Filter Bubble reinforcement of SNS recommendation algorithm identified in the Russia-Ukraine war (러시아-우크라이나 전쟁에서 파악된 SNS 추천알고리즘의 필터버블 강화현상 분석)

  • CHUN, Sang-Hun;CHOI, Seo-Yeon;SHIN, Seong-Joong
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.22 no.3
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    • pp.25-30
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    • 2022
  • This study is a study on the filter bubble reinforcement phenomenon of SNS recommendation algorithm such as YouTube, which is a characteristic of the Russian-Ukraine war (2022), and the victory or defeat factors of the hybrid war. This war is identified as a hybrid war, and the use of New Media based on the SNS recommendation algorithm is emerging as a factor that determines the outcome of the war beyond political leverage. For this reason, the filter bubble phenomenon goes beyond the dictionary meaning of confirmation bias that limits information exposed to viewers. A YouTube video of Ukrainian President Zelensky encouraging protests in Kyiv garnered 7.02 million views, but Putin's speech only 800,000, which is a evidence that his speech was not exposed to the recommendation algorithm. The war of these SNS recommendation algorithms tends to develop into an algorithm war between the US (YouTube, Twitter, Facebook) and China (TikTok) big tech companies. Influenced by US companies, Ukraine is now able to receive international support, and in Russia, under the influence of Chinese companies, Putin's approval rating is over 80%, resulting in conflicting results. Since this algorithmic empowerment is based on the confirmation bias of public opinion by 'filter bubble', the justification that a new guideline setting for this distortion phenomenon should be presented shortly is drawing attention through this Russia-Ukraine war.

The Effects of Characteristics of Media Facade on Customer's Preference (미디어파사드 특성이 문화예술공간의 선호도에 미치는 영향 연구)

  • Lee, Chul Soo;Nam, Sang Moon
    • The Journal of the Convergence on Culture Technology
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    • v.6 no.1
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    • pp.335-341
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    • 2020
  • As life, once immersed in labor, changes with values and lifestyles, individuals consume or participate in culture and arts for learning, meeting of intellectual needs, pleasure, and exchange. As culture and art spaces have increased in recent times, these spaces have been transformed into places to create, view and exchange culture and art, and to consume cultural goods. Culture and art spaces have created and developed new genres and technologies that give viewers the opportunity to communicate and participate, allowing them to understand and accumulate works of media. A media façade thus gives a preference to places for visitors by giving an impression over a short period of time in culture and art spaces that are not areas for exhibitions and performances, and providing an opportunity to more easily approach and understand works and culture and art spaces. A media façade is a type of medium that imparts aesthetics and information by installing LED lighting on the exterior wall of a building for the realization of media functions. In order to analyze the effect of the media façade on preferences for culture and art spaces, a research model was established with media façade characteristics as independent variables and preferences for culture and art spaces as dependent variables. As a result, media façade design and media features influenced satisfaction, while the media characteristics of the media façade influenced recommendation and revisiting, suggesting that many changes will take place in culture and art spaces.