• Asia pacific journal of information systems
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• v.17 no.4
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• pp.97-112
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• 2007

Most traditional newspaper publishers provide online editions to counter the competition of online news providers. However, the relationship between the online and print editions of the same newspaper has not been clearly defined. Some see the online newspaper as a substitute, while others consider it a complement. A 2002 NAA online newspaper consumer survey indicated that one-third of its respondents said they were now using the print newspaper less. Others have argued that the online edition will not wipe out print consumption, and may even complement it. While the print edition offers particular advantages such as portability, less eye strain, and the tactile experience of a printed page, the online edition also offers specific advantages such as access to breaking news, continually updated information, access to old archives, etc. All these factors would tend to lower the degree of interchangeability between the products. However, recent empirical studies show that the online edition is a substitute for rather than a complement of the print edition. Still, to some print readers, the online edition provides additional value. In this paper, by capturing the two different aspects of online editions the substitute aspect and the additional value added aspect as well as other available online alternatives, we develop an analytical model to derive the optimal production and distribution strategies of both online and print editions. Confronting the "free versus fee" issue, we show that it is optimal to provide an online version of the print newspaper for free to non-print subscribers. However, the amount of free news content that the publishers need to put on the Web depends on the available alternatives on the online market. The "fee" and "free" options both have merits and demerits as well. If the publisher charges for the online version of the print newspaper, she can generate revenue from the fee charged to online readers. However, doing so will limit the size of the online audience and further reduce online advertising revenue. At the same time, by providing a high-quality online version and charging for it, the price of the print newspaper must stay low in order to lure high valued readers. On the contrary, if the publisher provides an online version of the print newspaper for free, she can obtain a larger audience for the online version. At the same time, by providing a low-quality online newspaper, the publisher can increase the print newspaper price to get more revenue from high valued offline readers, although no revenue is incoming from online version readers. Through systematic measuring of all the pros and cons, our analysis shows that the optimal option is not "fee" but "free."

• Journal of the Korea Society of Computer and Information
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• v.20 no.11
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• pp.143-150
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• 2015

Social network services can be defined as an individual web page which enables online, human-relationship building by collecting useful information and sharing it with specific or unspecific people. Recently, as the social network services(SNS) such as Twitter and Facebook have been paid attention in many fields of the society. SNSs are also one of the fastest channels to get news which people may not be able to see on TV or newspaper. The number of people who feel they are benefiting from social network services are increasing dramatically. A number of researches about SNS are underway. The study based on the Technology Acceptance Model empirically investigates the relationship between characteristics of SNS (system, service, information, and emotional) and user satisfaction of SNS. The study also analyzes how the relationshipa between SNS characteristics, satisfaction and user acceptance are moderated by country type of SNS users and inclination toward SNS acceptance. To achieve these research purposes, the study conducted various statistical analyses using questionnaire of the Korean and Chinese SNS users. The results of the study are followings. First, SNS characteristics have a positive effect to the user satisfaction. Second, SNS satisfaction have a positive effect to the user acceptance. Third, the relationship between SNS characteristics and user satisfaction is moderated by the country type of SNS users and inclination toward SNS acceptance. The study results could provide some implications to researchers who have interest in studying SNS, also could help business managers to operate and develop their SNS site more effectively.

• Asia pacific journal of information systems
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• v.21 no.1
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• pp.103-122
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• 2011

Recently, numerous documents have been made available electronically. Internet search engines and digital libraries commonly return query results containing hundreds or even thousands of documents. In this situation, it is virtually impossible for users to examine complete documents to determine whether they might be useful for them. For this reason, some on-line documents are accompanied by a list of keywords specified by the authors in an effort to guide the users by facilitating the filtering process. In this way, a set of keywords is often considered a condensed version of the whole document and therefore plays an important role for document retrieval, Web page retrieval, document clustering, summarization, text mining, and so on. Since many academic journals ask the authors to provide a list of five or six keywords on the first page of an article, keywords are most familiar in the context of journal articles. However, many other types of documents could not benefit from the use of keywords, including Web pages, email messages, news reports, magazine articles, and business papers. Although the potential benefit is large, the implementation itself is the obstacle; manually assigning keywords to all documents is a daunting task, or even impractical in that it is extremely tedious and time-consuming requiring a certain level of domain knowledge. Therefore, it is highly desirable to automate the keyword generation process. There are mainly two approaches to achieving this aim: keyword assignment approach and keyword extraction approach. Both approaches use machine learning methods and require, for training purposes, a set of documents with keywords already attached. In the former approach, there is a given set of vocabulary, and the aim is to match them to the texts. In other words, the keywords assignment approach seeks to select the words from a controlled vocabulary that best describes a document. Although this approach is domain dependent and is not easy to transfer and expand, it can generate implicit keywords that do not appear in a document. On the other hand, in the latter approach, the aim is to extract keywords with respect to their relevance in the text without prior vocabulary. In this approach, automatic keyword generation is treated as a classification task, and keywords are commonly extracted based on supervised learning techniques. Thus, keyword extraction algorithms classify candidate keywords in a document into positive or negative examples. Several systems such as Extractor and Kea were developed using keyword extraction approach. Most indicative words in a document are selected as keywords for that document and as a result, keywords extraction is limited to terms that appear in the document. Therefore, keywords extraction cannot generate implicit keywords that are not included in a document. According to the experiment results of Turney, about 64% to 90% of keywords assigned by the authors can be found in the full text of an article. Inversely, it also means that 10% to 36% of the keywords assigned by the authors do not appear in the article, which cannot be generated through keyword extraction algorithms. Our preliminary experiment result also shows that 37% of keywords assigned by the authors are not included in the full text. This is the reason why we have decided to adopt the keyword assignment approach. In this paper, we propose a new approach for automatic keyword assignment namely IVSM(Inverse Vector Space Model). The model is based on a vector space model. which is a conventional information retrieval model that represents documents and queries by vectors in a multidimensional space. IVSM generates an appropriate keyword set for a specific document by measuring the distance between the document and the keyword sets. The keyword assignment process of IVSM is as follows: (1) calculating the vector length of each keyword set based on each keyword weight; (2) preprocessing and parsing a target document that does not have keywords; (3) calculating the vector length of the target document based on the term frequency; (4) measuring the cosine similarity between each keyword set and the target document; and (5) generating keywords that have high similarity scores. Two keyword generation systems were implemented applying IVSM: IVSM system for Web-based community service and stand-alone IVSM system. Firstly, the IVSM system is implemented in a community service for sharing knowledge and opinions on current trends such as fashion, movies, social problems, and health information. The stand-alone IVSM system is dedicated to generating keywords for academic papers, and, indeed, it has been tested through a number of academic papers including those published by the Korean Association of Shipping and Logistics, the Korea Research Academy of Distribution Information, the Korea Logistics Society, the Korea Logistics Research Association, and the Korea Port Economic Association. We measured the performance of IVSM by the number of matches between the IVSM-generated keywords and the author-assigned keywords. According to our experiment, the precisions of IVSM applied to Web-based community service and academic journals were 0.75 and 0.71, respectively. The performance of both systems is much better than that of baseline systems that generate keywords based on simple probability. Also, IVSM shows comparable performance to Extractor that is a representative system of keyword extraction approach developed by Turney. As electronic documents increase, we expect that IVSM proposed in this paper can be applied to many electronic documents in Web-based community and digital library.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.57-77
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• 2013

Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.