• Title/Summary/Keyword: media recommendation

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The impact of virtual Brand experience using Metaverse on Interest, Immersion, and Recommendation intention (메타버스를 활용한 가상 브랜드 체험이 흥미, 몰입 및 추천의도에 미치는 영향)

  • Chang Sung Bok
    • Smart Media Journal
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    • v.12 no.7
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    • pp.84-92
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    • 2023
  • This study tested the hypothesis through confirmatory factor analysis to confirm the relationship between Brand experiences (Deviant, Entertainment, and Aesthetic experiences) in Metaverse on Interest and Immersion, and to verify whether these Interests and Immersion have a significant impact on Recommendation intention. As a result of the study, it was confirmed that all Brand experience factors had a positive (+) effect on Interest and Immersion, Interest had a positive (+) effect on Immersion, and Interest and Immersion had a positive (+) effect on Recommendation intention.

Keyword-Based Contents Recommendation Web Service (키워드 기반 콘텐츠 추천 웹서비스)

  • Park, Dong-Jin;Kim, Min-Geun;Song, Hyeon-Seop;Yoon, Seok-Min;Kim, Youngjong
    • Proceedings of the Korea Information Processing Society Conference
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    • 2022.05a
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    • pp.346-348
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    • 2022
  • Media Contents Recommendation Web Service (service name 'mobodra') is a web service that analyzes media types and genre tastes for each user and recommends content accordingly. Users select some of the works randomly provided on the web when signing up for membership and analyze their tastes based on this. Based on this analysis, preferred content for each user is recommended. In this paper, we implement a content recommendation algorithm through item-based collaborative filtering. When the user's activity data or preference is re-examined, the above process is executed again to update the user's taste.

Visualization of movie recommendation system using the sentimental vocabulary distribution map

  • Ha, Hyoji;Han, Hyunwoo;Mun, Seongmin;Bae, Sungyun;Lee, Jihye;Lee, Kyungwon
    • Journal of the Korea Society of Computer and Information
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    • v.21 no.5
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    • pp.19-29
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    • 2016
  • This paper suggests a method to refine a massive collective intelligence data, and visualize with multilevel sentiment network, in order to understand information in an intuitive and semantic way. For this study, we first calculated a frequency of sentiment words from each movie review. Second, we designed a Heatmap visualization to effectively discover the main emotions on each online movie review. Third, we formed a Sentiment-Movie Network combining the MDS Map and Social Network in order to fix the movie network topology, while creating a network graph to enable the clustering of similar nodes. Finally, we evaluated our progress to verify if it is actually helpful to improve user cognition for multilevel analysis experience compared to the existing network system, thus concluded that our method provides improved user experience in terms of cognition, being appropriate as an alternative method for semantic understanding.

A study of Metadata design for Digital Content Marketplace based on Interactive Media (양방향매체 기반에 디지털콘텐츠 마켓플레이스를 위한 메타데이터 설계에 관한 연구)

  • Kwon, Byung-Il;Moon, Nam-Mee
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.9 no.3
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    • pp.155-164
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    • 2009
  • Digital Content Marketplace based on Interactive Media is defmed as the marketplace for content service between contents supplier and consumer through iDTV environment. This Marketplace is increasing interest to u-Life service with Digital Environment. To Interactive Media, it can contribute to enhance its effectiveness by developing various contents and service model in the initial phase of broadcasting-communication convergence. This study designed metadata using Digital Content marketplace based on Interactive Media. Specially the matadata designing include recommendation-tag for supply supplementary content. It can support self-directed action. Through basic metadata with weight value, it is designed to support supplementary content customer to want on the marketplace. Recommendation-System can be built by many method and to recommend the service content including explicit properties using collaborative filtering method can solve limitations in existing content recommendation.

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A Research on the Method of Automatic Metadata Generation of Video Media for Improvement of Video Recommendation Service (영상 추천 서비스의 개선을 위한 영상 미디어의 메타데이터 자동생성 방법에 대한 연구)

  • You, Yeon-Hwi;Park, Hyo-Gyeong;Yong, Sung-Jung;Moon, Il-Young
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2021.10a
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    • pp.281-283
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    • 2021
  • The representative companies mentioned in the recommendation service in the domestic OTT(Over-the-top media service) market are YouTube and Netflix. YouTube, through various methods, started personalized recommendations in earnest by introducing an algorithm to machine learning that records and uses users' viewing time from 2016. Netflix categorizes users by collecting information such as the user's selected video, viewing time zone, and video viewing device, and groups people with similar viewing patterns into the same group. It records and uses the information collected from the user and the tag information attached to the video. In this paper, we propose a method to improve video media recommendation by automatically generating metadata of video media that was written by hand.

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Clothing-Recommendation system based on emotion and weather information (감정과 날씨 정보에 따른 의상 추천 시스템)

  • Ugli, Sadriddinov Ilkhomjon Rovshan;Park, Doo-Soon
    • Proceedings of the Korea Information Processing Society Conference
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    • 2021.11a
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    • pp.528-531
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    • 2021
  • Nowadays recommendation systems are so ubiquitous, where our many decisions are being done by the means of them. We can see recommendation systems in all areas of our daily life. Therefore the research of this sphere is still so active. So far many research papers were published for clothing recommendations as well. In this paper, we propose the clothing-recommendation system according to user emotion and weather information. We used social media to analyze users' 6 basic emotions according to Paul Eckman theory and match the colour of clothing. Moreover, getting weather information using visualcrossing.com API to predict the kind of clothing. For sentiment analysis, we used Emotion Lexicon that was created by using Mechanical Turk. And matching the emotion and colour was done by applying Hayashi's Quantification Method III.

Design and implementation of a music recommendation model through social media analytics (소셜 미디어 분석을 통한 음악 추천 모델의 설계 및 구현)

  • Chung, Kyoung-Rock;Park, Koo-Rack;Park, Sang-Hyock
    • Journal of Convergence for Information Technology
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    • v.11 no.9
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    • pp.214-220
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    • 2021
  • With the rapid spread of smartphones, it has become common to listen to music everywhere, just like background music in life, so it is necessary to create a music database that can make recommendations according to individual circumstances and conditions. This paper proposes a music recommendation model through social media. Since emotions, situations, time of day, weather, etc. are included in hashtags, it is possible to build a social media-based database that reflects the opinions of various people with collective intelligence. We use web crawling to collect and categorize different hashtags from posts with music title hashtags to use real listeners' opinions about music in a database. Data from social media is used to create a music database, and music is classified in a different way from collaborative filtering, which is mainly used by existing music platforms.

A Study on the Effect of Characteristics of Online Streaming Course on Learning Satisfaction and Recommendation Intention (온라인 스트리밍 수업의 특성이 학습 만족도와 추천의도에 미치는 영향 분석 연구)

  • Zhu, LiuCun;Yang, HuiJun;Jiang, Xuejin;Hwang, HaSung
    • Journal of Internet Computing and Services
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    • v.23 no.5
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    • pp.59-68
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    • 2022
  • As real-time live streaming broadcasting and non-face-to-face classes are spreading in the Corona era, it is time to take academic interest in online streaming classes. In particular, it is important to clarify why users use online streaming classes. Therefore, this study proposes social presence, interest, convenience of use, and interactivity as characteristics of online streaming classes, and aims to verify how these characteristics affect learning satisfaction and furthermore, recommendation intention. As a result of conducting a survey on 338 Chinese collegestudents, it was found that interactivity, social presence, and interest had a positive effect on learning satisfaction, but the effect of ease did not appear. On the other hand, it was confirmed that learning satisfaction had a positive effect on the online streaming class recommendation intention.

Automatic Generation of Video Metadata for the Super-personalized Recommendation of Media

  • Yong, Sung Jung;Park, Hyo Gyeong;You, Yeon Hwi;Moon, Il-Young
    • Journal of information and communication convergence engineering
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    • v.20 no.4
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    • pp.288-294
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    • 2022
  • The media content market has been growing, as various types of content are being mass-produced owing to the recent proliferation of the Internet and digital media. In addition, platforms that provide personalized services for content consumption are emerging and competing with each other to recommend personalized content. Existing platforms use a method in which a user directly inputs video metadata. Consequently, significant amounts of time and cost are consumed in processing large amounts of data. In this study, keyframes and audio spectra based on the YCbCr color model of a movie trailer were extracted for the automatic generation of metadata. The extracted audio spectra and image keyframes were used as learning data for genre recognition in deep learning. Deep learning was implemented to determine genres among the video metadata, and suggestions for utilization were proposed. A system that can automatically generate metadata established through the results of this study will be helpful for studying recommendation systems for media super-personalization.

A Study on Recommendation Systems based on User multi-attribute attitude models and Collaborative filtering Algorithm (다속성 태도 모델과 협업적 필터링 기반 장소 추천 연구)

  • Ahn, Byung-Ik;Jung, Ku-Imm;Choi, Hae-Lim
    • Smart Media Journal
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    • v.5 no.2
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    • pp.84-89
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    • 2016
  • For a place-recommendation model based on user's behavior and multi-attribute attitude in this thesis. We focus groups that show similar patterns of visiting restaurants and then compare one and the other. We make use of The Fishbein Equation, Pearson's Correlation Coefficient to calculate multi-attribute attitude scores. Furthermore, We also make use of Preference Prediction Algorithm and Distance based method named "Euclidean Distance" to provide accurate results. We can demonstrate how excellent this system is through several experiments carried out with actual data.