• Title/Summary/Keyword: social content search

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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.

Sorting Instagram Hashtags all the Way throw Mass Tagging using HITS Algorithm

  • D.Vishnu Vardhan;Dr.CH.Aparna
    • International Journal of Computer Science & Network Security
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    • v.23 no.11
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    • pp.93-98
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    • 2023
  • Instagram is one of the fastest-growing online photo social web services where users share their life images and videos with other users. Image tagging is an essential step for developing Automatic Image Annotation (AIA) methods that are based on the learning by example paradigm. Hashtags can be used on just about any social media platform, but they're most popular on Twitter and Instagram. Using hashtags is essentially a way to group together conversations or content around a certain topic, making it easy for people to find content that interests them. Practically on average, 20% of the Instagram hashtags are related to the actual visual content of the image they accompany, i.e., they are descriptive hashtags, while there are many irrelevant hashtags, i.e., stophashtags, that are used across totally different images just for gathering clicks and for search ability enhancement. Hence in this work, Sorting instagram hashtags all the way through mass tagging using HITS (Hyperlink-Induced Topic Search) algorithm is presented. The hashtags can sorted to several groups according to Jensen-Shannon divergence between any two hashtags. This approach provides an effective and consistent way for finding pairs of Instagram images and hashtags, which lead to representative and noise-free training sets for content-based image retrieval. The HITS algorithm is first used to rank the annotators in terms of their effectiveness in the crowd tagging task and then to identify the right hashtags per image.

Information Search Factor of Consumer Behavior -In case of purchasing electric goods- (소비자의 정보탐색 행동에 관한 연구 -가전제품 구매행동을 중심으로-)

  • 강미옥
    • Journal of the Korean Home Economics Association
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    • v.30 no.1
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    • pp.149-161
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    • 1992
  • The purpose of this study is to analyze information search activity in purchasing behavior of household electric goods. Qusetionare survey method was used in this research. The sample was taken from 302 housewives living in Seoul, from 9th of Nov. to 20th of Nov, in 1991. Used statical methods were Frequency, Percentage, Crosstab, Anova, and Regression Analysis. The major findings are summarized as follows : 1) Component elements of information search : The means of acquiring information is that friends, neighbors, sales are most. A cause of choosing information is the sequence of satisfaction after using, easiness of interaction. The time in choosing goods is more month. 2) Component element of information search as social economic status housewife : children numbers and means of acquiring information(P<.01), education and a cause of choosing information(P<.05), life cost per month and a cause of choosing information(P<.05), social economic status and a time information search are significant. 3) A perception of risk as searching information : Among searching content of information a price influence a perception of risk. 4) Content of searching information and satisfaction of purchasing experience : Best choice is significant as quality of goods, difference of quality is significant as safety and degree of offering information is significant as a brand. 5) Satisfaction of purchasing experience following practical use of information : Best choice is significant as viewing of an exhibit and opinion of user. Difference of quality is not significant as any vairable. Degree of offer information influence searching pamphlet, searching an advertisement and opinion of user. 6) A perception of risk following source of an information : A perception of risk is most influenced by pamphlet.

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Improving Diversity of Keyword Search on Graph-structured Data by Controlling Similarity of Content Nodes (콘텐트 노드의 유사성 제어를 통한 그래프 구조 데이터 검색의 다양성 향상)

  • Park, Chang-Sup
    • The Journal of the Korea Contents Association
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    • v.20 no.3
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    • pp.18-30
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    • 2020
  • Recently, as graph-structured data is widely used in various fields such as social networks and semantic Webs, needs for an effective and efficient search on a large amount of graph data have been increasing. Previous keyword-based search methods often find results by considering only the relevance to a given query. However, they are likely to produce semantically similar results by selecting answers which have high query relevance but share the same content nodes. To improve the diversity of search results, we propose a top-k search method that finds a set of subtrees which are not only relevant but also diverse in terms of the content nodes by controlling their similarity. We define a criterion for a set of diverse answer trees and design two kinds of diversified top-k search algorithms which are based on incremental enumeration and A heuristic search, respectively. We also suggest an improvement on the A search algorithm to enhance its performance. We show by experiments using real data sets that the proposed heuristic search method can find relevant answers with diverse content nodes efficiently.

Page Group Search Model : A New Internet Search Model for Illegal and Harmful Content (페이지 그룹 검색 그룹 모델 : 음란성 유해 정보 색출 시스템을 위한 인터넷 정보 검색 모델)

  • Yuk, Hyeon-Gyu;Yu, Byeong-Jeon;Park, Myeong-Sun
    • Journal of KIISE:Computer Systems and Theory
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    • v.26 no.12
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    • pp.1516-1528
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    • 1999
  • 월드 와이드 웹(World Wide Web)에 존재하는 음란성 유해 정보는 많은 국가에서 사회적인 문제를 일으키고 있다. 그러나 현재 음란성 유해 정보로부터 미성년자를 보호하는 실효성 있는 방법은 유해 정보 접근 차단 프로그램을 사용하는 방법뿐이다. 유해 정보 접근 차단 프로그램은 기본적으로 음란성 유해 정보를 포함한 유해 정보 주소 목록을 기반으로 사용자의 유해 정보에 대한 접근을 차단하는 방식으로 동작한다.그런데 대규모 유해 정보 주소 목록의 확보를 위해서는 월드 와이드 웹으로부터 음란성 유해 정보를 자동 색출하는 인터넷 정보 검색 시스템의 일종인 음란성 유해 정보 색출 시스템이 필요하다. 그런데 음란성 유해 정보 색출 시스템은 그 대상이 사람이 아닌 유해 정보 접근 차단 프로그램이기 때문에 일반 인터넷 정보 검색 시스템과는 달리, 대단히 높은 검색 정확성을 유지해야 하고, 유해 정보 접근 차단 프로그램에서 관리가 용이한 검색 목록을 생성해야 하는 요구 사항을 가진다.본 논문에서는 기존 인터넷 정보 검색 모델이 "문헌"에 대한 잘못된 가정 때문에 위 요구사항을 만족시키지 못하고 있음을 지적하고, 월드 와이드 웹 상의 문헌에 대한 새로운 정의와 이를 기반으로 위의 요구사항을 만족하는 검색 모델인 페이지 그룹 검색 모델을 제안한다. 또한 다양한 실험과 분석을 통해 제안하는 모델이 기존 인터넷 정보 검색 모델보다 높은 정확성과 빠른 검색 속도, 그리고 유해 정보 접근 차단 프로그램에서의 관리가 용이한 검색 목록을 생성함을 보인다.Abstract Illegal and Harmful Content on the Internet, especially content for adults causes a social problem in many countries. To protect children from harmful content, A filtering software, which blocks user's access to harmful content based on a blocking list, and harmful content search system, which is a special purpose internet search system to generate the blocking list, are necessary. We found that current internet search models do not satisfy the requirements of the harmful content search system: high accuracy in document analysis, fast search time, and low overhead in the filtering software.In this paper we point out these problems are caused by a mistake in a document definition of the current internet models and propose a new internet search model, Page Group Search Model. This model considers a document as a set of pages that are made for one subject. We suggest a Group Construction algorithm and a Group Evaluation algorithm. And we perform experiments to prove that Page Group Search Model satisfies the requirements.uirements.

An Evaluation Method for Contents Importance Based on Twitter Characteristics (트위터 특징에 기반한 콘텐츠 중요성 평가 기법)

  • Lee, Euijong;Kim, Jeong-Dong;Baik, Doo-Kwon
    • Journal of KIISE
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    • v.41 no.12
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    • pp.1136-1144
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    • 2014
  • Twitter is a social network service that generates about 140 million contents a day. Contents of Twitter contain a variety of information and many researchers research those in various fields. In this research, we propose a method for evaluating the importance of content based on characteristics of Twitter. We have found that number of follower means user's popularity and Re-tweet that means the popularity of content. We perform experiments about proposed method using real Twitter data for proving effectiveness of proposed method. Also, we found information providers in Twitter are public user who represent a company or a representative of a specific group.

Use of Emoji as a Marketing Tool: An Exploratory Content Analysis

  • Mathews, Stanley;Lee, Seung-Eun
    • Fashion, Industry and Education
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    • v.16 no.1
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    • pp.46-55
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    • 2018
  • The purpose of this exploratory study was to enhance the understanding of how brands tilize emojis in their marketing practices. A content analysis was conducted utilizing Google News as a search tool to access articles containing information pertaining to the use of emojis by brands. The combination of keywords used for the search were "emoji", "business", and "marketing". The search was narrowed down to the period of January $1^{st}$, 2014 - November $29^{th}$, 2017. This method generated a total of 604 trade publications with 55 of them providing information pertaining to specific brands and their use of emojis in their marketing strategies. A content analysis of trade publications has revealed that a variety of marketers have utilized emojis in their brand marketing practices. The entertainment, service, and food/drink industries have predominantly utilized emojis in their marketing practices, and their primary purpose for using emojis was to increase consumer engagement. Brands applied most of these emoji marketing strategies to an online or digital setting, whether it was social media pages, mobile applications, or any other form of computer-based marketing. Although there are limitations to this exploratory research in terms of its methodology, the findings of this study provide interesting insights into the potential of emojis as a marketing tool.

Marketer Generated Content on Social Media: How to Support Corporate Online Distribution

  • ZHONG, Xin;YAN, Jinzhe
    • Journal of Distribution Science
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    • v.20 no.3
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    • pp.33-43
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    • 2022
  • Purpose: More and more marketers use social media platforms to create and spread information called Marketer Generated Content (MGC) to inform consumers of products. MGC often embeds product purchase links, thus directing consumers to online distribution channels for online purchases. This study examined the effect of social media MGC on consumers' willingness to buy online in the anchor of consumers' perspective to answer the question of "how social media generated content support corporate online distribution". Research design, data, and methodology: According to the means-end-chain theory, we introduce perceived value and continuous following intention as chain mediators to explain the mechanism of MGC influence on consumers' online purchase intention and consider product type to discuss boundary conditions. Two experiments were designed to test hypothesizes. Results and Conclusion: First, emotional MGC (vs. informational MGC) has lower (higher) perceived utility (hedonic) value. Second, perceived value has a significant mediate role in the effect of MGC on continuous following intention. Third, perceived value and continuous following intention significantly and sequentially mediated the effect of MGC on online purchase intention. Through the sequential mediations of perceived utility value and continuous following intention, Informational MGC of search products significantly increase online purchase intentions. Another parallel sequential mediation, including perceived hedonic, emotional MGC of experience products, partially enhanced online purchase intentions. Finally, this study gives implications for how corporates can use social media MGC to promote product sales online.

The Kernel Trick for Content-Based Media Retrieval in Online Social Networks

  • Cha, Guang-Ho
    • Journal of Information Processing Systems
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    • v.17 no.5
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    • pp.1020-1033
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    • 2021
  • Nowadays, online or mobile social network services (SNS) are very popular and widely spread in our society and daily lives to instantly share, disseminate, and search information. In particular, SNS such as YouTube, Flickr, Facebook, and Amazon allow users to upload billions of images or videos and also provide a number of multimedia information to users. Information retrieval in multimedia-rich SNS is very useful but challenging task. Content-based media retrieval (CBMR) is the process of obtaining the relevant image or video objects for a given query from a collection of information sources. However, CBMR suffers from the dimensionality curse due to inherent high dimensionality features of media data. This paper investigates the effectiveness of the kernel trick in CBMR, specifically, the kernel principal component analysis (KPCA) for dimensionality reduction. KPCA is a nonlinear extension of linear principal component analysis (LPCA) to discovering nonlinear embeddings using the kernel trick. The fundamental idea of KPCA is mapping the input data into a highdimensional feature space through a nonlinear kernel function and then computing the principal components on that mapped space. This paper investigates the potential of KPCA in CBMR for feature extraction or dimensionality reduction. Using the Gaussian kernel in our experiments, we compute the principal components of an image dataset in the transformed space and then we use them as new feature dimensions for the image dataset. Moreover, KPCA can be applied to other many domains including CBMR, where LPCA has been used to extract features and where the nonlinear extension would be effective. Our results from extensive experiments demonstrate that the potential of KPCA is very encouraging compared with LPCA in CBMR.

Method for improving search efficiency using relation of anatomical structure from Donguibogam(東醫寶鑑) ("동의보감"에 기재된 인체 용어 관계를 이용한 검색효율성 향상 방법)

  • Song, In-Woo;Lee, Byung-Wook
    • Journal of Korean Medical classics
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    • v.25 no.4
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    • pp.105-113
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    • 2012
  • Objectives : Acquiring information from symptoms is one of the important method to gain clinically available information in korean medicine. Therefore, up to now, study of symptom terms was frequently implemented in promotion of various information project. In data extraction methods using symptom information from DB, information search using synonym and method using ontology is studied and utilized. However, considering concept of symptom has essential information of appeared body area and phenomenon we think that extending synonym and ontology relationship in symptom terms can be useful for search and set to this study. Methods : We collect terms relevant to human body area and structure described in Donguibogam. Synonymous relationship between collected terms is organized. Relationship between collected terms is build to human-body-knowledge table which has form of Concept+Relation+Concept. Type of relationship is limited on a range of expressing content about parts of human body. Result & Conclusion : Search condition is generated automatically using relationship of the upper area in knowledge table contents. Information of next and previous acupuncture point, upper and lower acupuncture point, left and right acupuncture point can be searched using information of acupuncture point location, order, relative position in area, direction in knowledge table contents.