• Title/Summary/Keyword: media recommendation

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A Study on Artificial Intelligence Based Business Models of Media Firms

  • Song, Minzheong
    • International journal of advanced smart convergence
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    • v.8 no.2
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    • pp.56-67
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    • 2019
  • The aim of this study is to develop Artificial Intelligence (AI) based business models of media firms. We define AI and discuss 'AI activity model'. The practices of the efficiency model are home equipment-based personalization and media content recommendation. The practices of the expert model are media content commissioning, content rights negotiation, copyright infringement, and promotion. The practices of the effectiveness model are photo & video auto-tagging and auto subtitling & simultaneous translation. The practices of the innovation model are content script creation and metadata management. The related use cases from 2012 to 2017 are introduced along the four activity models of AI. In conclusion, we propose for media companies to fully utilize the AI for transforming from traditional to successful digital media firms.

Personalized Travel Path Recommendations with Social Life Log (소셜 라이프 로그를 이용한 개인화된 여행 경로 추천)

  • Paul, Aniruddha;Lim, Jongtae;Bok, Kyoungsoo;Yoo, Jasesoo
    • Proceedings of the Korea Contents Association Conference
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    • 2017.05a
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    • pp.453-454
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    • 2017
  • The travellers using social media leave their location history in the form of trajectories. These trajectories can be bridged for acquiring information, required for future recommendation for the future travelers, who are new to that location, providing all sort of information. In this paper, we propose a personalized travel path recommendation scheme based on social life log. By taking advantage of two kinds of social media such as travelogue and community contributed photos, the proposed scheme can not only be personalized to user's travel interest but also be able to recommend a travel path rather than individual Points of Interest (POIs). It also maps both user's and routes' textual descriptions to the topical package space to get user topical package model and route topical package model (i.e., topical interest, cost, time and season).

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Social Network Based Music Recommendation System (소셜네트워크 기반 음악 추천시스템)

  • Park, Taesoo;Jeong, Ok-Ran
    • Journal of Internet Computing and Services
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    • v.16 no.6
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    • pp.133-141
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    • 2015
  • Mass multimedia contents are shared through various social media servies including social network service. As social network reveals user's current situation and interest, highly satisfactory personalized recommendation can be made when such features are applied to the recommendation system. In addition, classifying the music by emotion and using analyzed information about user's recent emotion or current situation by analyzing user's social network, it will be useful upon recommending music to the user. In this paper, we propose a music recommendation method that makes an emotion model to classify the music, classifies the music according to the emotion model, and extracts user's current emotional state represented on the social network to recommend music, and evaluates the validity of our method through experiments.

Rating Individual Food Items of Restaurant Menu based on Online Customer Reviews using Text Mining Technique (신뢰성있는 온라인 고객 리뷰 텍스트 마이닝 기반 식당 개별 음식 아이템 평가)

  • Syed, Muzamil Hussain;Chung, Sun-Tae
    • Proceedings of the Korea Information Processing Society Conference
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    • 2020.05a
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    • pp.389-392
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    • 2020
  • The growth in social media, blogs and restaurant listing directories have led to increasing customer reviews about restaurants, their quality of food items and services available on the internet. These user reviews offer a massive amount of valuable information that can be used for various decision-making purposes. Currently, most food recommendation sites provide recommendation scores about restaurants rather than food items of the restaurant and the provided recommendation scores may be biased since they are calculated only from user reviews listed only in their sites. Usually, people wants a reliable recommendation about foods, not restaurant. In this paper, we present a reliable Korean food items rating method; we first extract food items by applying NER technique to restaurant reviews collected from many Korean restaurant recommendation web sites, blogs and web data. Then, we apply lexicon-based sentiment analysis on collected user reviews and predict people's opinions as sentiment polarity scores (+1 for positive; -1 for negative; 0 for neutral). Finally, by taking average of all calculated polarity scores about a food item, we obtain a rating to individual menu items of the restaurant. The proposed food item rating is more reliable since it does not depend on reviews of only one site.

User-Centric Conflict Management for Media Services Using Personal Companions

  • Shin, Choon-Sung;Yoon, Hyo-Seok;Woo, Woon-Tack
    • ETRI Journal
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    • v.29 no.3
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    • pp.311-321
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    • 2007
  • In this paper, we propose a user-centric conflict management method for media services which exploits personal companions for the harmonious detection and resolution of service conflicts. To detect conflicts based on the varying characteristics of individual users, the proposed method exploits the unified context describing all users attempting to access media services. It recommends and mediates users' preferred media contents through a shared screen and personal companions to resolve the detected conflicts. During the recommendation, a list of preferred media contents is displayed on the shared screen, and a personally preferred content list is shown on the user's personal companion comprising the selection of media contents. Mediation assists the selection of a consensual service by gathering the users' selections and highlighting the common media contents. In experiments carried out in a ubiHome, we observed that recommendations and mediation are useful in harmoniously resolving conflicts by encouraging user participation in conflict situations.

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A Study on the Curation Factors through Reverse Engineering Design of YouTube Algorithm - Focusing on Gender Keyword Search (유튜브 알고리즘의 역공학설계를 통한 큐레이션 요인 연구 - 성별 키워드 검색을 중심으로)

  • Bae, Seung-Ju;Lee, Sang-Ho
    • Journal of the Korea Convergence Society
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    • v.13 no.3
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    • pp.133-146
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    • 2022
  • Despite the fact that Internet users around the world watch YouTube every day, very few users accurately recognize the recommendation algorithm for search results, and Google and YouTube are not disclosing it. Researchers tried to explore the undisclosed algorithm of YouTube in a reverse engineering design method, find key factors, and check the logical structure in which media platform operators recommend keyword search results and arrange them on the screen. Therefore, researchers studied the basic content priority factors through several months of discussion and data collection, and tried to reverse engineer the influencing factors based on the recommendation results according to male and female gender among the collected keyword search results. Although researchers' design only analyzed some of the almost infinite level of data uploaded and viewed for more than hundreds of hours every hour, these exploratory attempts will study media platform algorithms in the future, understand the intentions of operators, and protect users. thought it could be done.

A Research on Image Metadata Extraction through YCrCb Color Model Analysis for Media Hyper-personalization Recommendation (미디어 초개인화 추천을 위한 YCrCb 컬러 모델 분석을 통한 영상의 메타데이터 추출에 대한 연구)

  • Park, Hyo-Gyeong;Yong, Sung-Jung;You, Yeon-Hwi;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.277-280
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    • 2021
  • Recently as various contents are mass produced based on high accessibility, the media contents market is more active. Users want to find content that suits their taste, and each platform is competing for personalized recommendations for content. For an efficient recommendation system, high-quality metadata is required. Existing platforms take a method in which the user directly inputs the metadata of an image. This will waste time and money processing large amounts of data. In this paper, for media hyperpersonalization recommendation, keyframes are extracted based on the YCrCb color model of the video based on movie trailers, movie genres are distinguished through supervised learning of artificial intelligence and In the future, we would like to propose a utilization plan for generating metadata.

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Personalized Bookmark Search Word Recommendation System based on Tag Keyword using Collaborative Filtering (협업 필터링을 활용한 태그 키워드 기반 개인화 북마크 검색 추천 시스템)

  • Byun, Yeongho;Hong, Kwangjin;Jung, Keechul
    • Journal of Korea Multimedia Society
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    • v.19 no.11
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    • pp.1878-1890
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    • 2016
  • Web 2.0 has features produced the content through the user of the participation and share. The content production activities have became active since social network service appear. The social bookmark, one of social network service, is service that lets users to store useful content and share bookmarked contents between personal users. Unlike Internet search engines such as Google and Naver, the content stored on social bookmark is searched based on tag keyword information and unnecessary information can be excluded. Social bookmark can make users access to selected content. However, quick access to content that users want is difficult job because of the user of the participation and share. Our paper suggests a method recommending search word to be able to access quickly to content. A method is suggested by using Collaborative Filtering and Jaccard similarity coefficient. The performance of suggested system is verified with experiments that compare by 'Delicious' and "Feeltering' with our system.

An Exploratory Study on the Information Recipients' Acceptance(Comprehension) and Diffusion: According to the Authenticity of the News(Real News vs. Fake News) and Need for Cognition (뉴스진위 및 인지욕구에 따른 정보수용자의 수용(이해)과 확산영향에 대한 탐색적 연구)

  • Cho, Ara;Kwon, Soonjae
    • Knowledge Management Research
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    • v.20 no.2
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    • pp.87-103
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    • 2019
  • The purpose of this study was to explore the factors influencing acceptance (e.g., comprehension,) and diffusion of information recipients' by depending on the authenticity of news. Specifically, this study has examined the effects of the news contents(political vs. general), need for cognition(high vs. low) and authenticity of the News(real news vs. fake news) on both acceptance and diffusion of news. Based on previous work, this study has developed a conceptual model to present each research hypothesis and tested it by conducting experiments as the follows. As a result, according to the authenticity of the news and the contents of the news (political and general), the acceptance of political contents was high regardless of the authenticity of the news, and the acceptance of real news was higher than that of fake news. However, in the proliferation (comment), both the political contents and the general contents showed the characteristic of spreading (commenting) fake news rather than real news. contrary to this, the cognitive level did not show any significant difference in acceptance (understanding) and proliferation (comment, sharing, recommendation). This study provides academic implications in that it examines the influences of accepting (comprehension) and diffusion (comment, sharing, recommendation) of real news and fake news. It also provides practical implications for responding to fake news and new marketing strategies in an environment where contents are delivered through diverse social media.

Semi-supervised Cross-media Feature Learning via Efficient L2,q Norm

  • Zong, Zhikai;Han, Aili;Gong, Qing
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.3
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    • pp.1403-1417
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    • 2019
  • With the rapid growth of multimedia data, research on cross-media feature learning has significance in many applications, such as multimedia search and recommendation. Existing methods are sensitive to noise and edge information in multimedia data. In this paper, we propose a semi-supervised method for cross-media feature learning by means of $L_{2,q}$ norm to improve the performance of cross-media retrieval, which is more robust and efficient than the previous ones. In our method, noise and edge information have less effect on the results of cross-media retrieval and the dynamic patch information of multimedia data is employed to increase the accuracy of cross-media retrieval. Our method can reduce the interference of noise and edge information and achieve fast convergence. Extensive experiments on the XMedia dataset illustrate that our method has better performance than the state-of-the-art methods.