• Journal of Internet Computing and Services
• /
• v.17 no.6
• /
• pp.133-141
• /
• 2016

Recent web services provide tagging function to users, and let them express the topic of the contents of their articles. Moreover, we can extract context information like emotion of the writer efficiently by using tags attached to the articles or images. And we are able to better understand article than traditional algorithm. (eg. TF-IDF) Therefore, if we use tags in recommendation system, we can recommend high quality resources to the users. This study proposes a recommendation method that provide web resources (articles, users) through simple algorithm based on related tag set extracted from the article. Through the experiments, we show that the result was satisfactory, and we measure the satisfaction of users.

• Journal of Digital Convergence
• /
• v.18 no.1
• /
• pp.301-307
• /
• 2020

In recent years, a recommendation system is introduced in many different fields with the beginning of the 5G era and making a considerably prominent appearance mainly in books, movies, and music. In such a recommendation system, however, the preference degrees of users are subjective and uncertain, which means that it is difficult to provide accurate recommendation service. There should be huge amounts of learning data and more accurate estimation technologies in order to improve the performance of a recommendation system. Trying to solve this problem, this study proposed a movie recommendation system based on Fuzzy-AHP and Word2vec. The proposed system used Fuzzy-AHP to make objective predictions about user preference and Word2vec to classify scraped data. The performance of the system was assessed by measuring the accuracy of Word2vec outcomes based on grid search and comparing movie ratings predicted by the system with those by the audience. The results show that the optimal accuracy of cross validation was 91.4%, which means excellent performance. The differences in move ratings between the system and the audience were compared with the Fuzzy-AHP system, and it was superior at approximately 10%.

• Proceedings of the Korea Contents Association Conference
• /
• 2006.11a
• /
• pp.745-749
• /
• 2006

The appearance of wirelsss internet service made accessing easier than existing mobile devices. Due to the properity of mobile devices, we can easily obtain his/her profile information compared to a wired internet service. It enables to provide a personalized service through mobile devices. In this paper, we propose a recommendation service based on collaborative filtering method and a content push service. Using users' profile information, we recommand a target user's favoriate content. The recommanded contents are stored in mobile device through the push service. When we connect a wireless internet service, our mobile push service starts to cache from user's favorite contents. Especially, when we select a large mobile content, our system can reduce a download time by using our recommandation service. Also, in case of a connectionless, we can use a cached data from pushed content in our mobile device.

• International journal of advanced smart convergence
• /
• v.8 no.4
• /
• pp.40-46
• /
• 2019

The current education is becoming more extensive with the application of various teaching methods. This is a problem that is so distributed that it is difficult for users to find the data and it takes a long time to find the information they need. Currently, various educational services, materials, and instruments are developed and scattered. Therefore, it is important to raise students' awareness of aptitude and career path with customized education tailored to students. Conventional education platforms have very difficult to choose the right materials for students because of the spread of educational programs and institution materials. To solve this, we propose a customized recommendation approach to recommend customized educational service materials and institution for students to teachers, which helps teachers conveniently choose materials suitable for their respective environments. On this new platform, the CNN algorithm provides recommended content for classes and students. For real service on the educational service platform, we implement this system for Jeil edus business. Through this mechanism, we expect to improve the quality of education by helping to select the right service.

• Journal of Korea Multimedia Society
• /
• v.9 no.12
• /
• pp.1709-1718
• /
• 2006

Users are able to use the information and service more free than previous wire network due to development of wireless network and device. For this reason, various studies on ubiquitous networks have been conducted. Various contexts brought in this ubiquitous environment, have recognized user's action through sensors. This results in the provision of better services. Because services exist in various places in ubiquitous networks, the application has the time of services searching. In addition, user's context is very dynamic, so a method needs to be found to recommend services to user by context. Therefore, techniques for reducing the time of service and increasing accuracy of recommendation are being studied. But it is difficult to quickly and appropriately provide large numbers of services, because only basic context information is stored. For this reason, we suggest DUPS(Dimension User Profile System), which stores location, time, and frequency information of often used services. Because previous technique used to simple information for recommending service without predicting services which is going to use on future, we can provide better service, and improve accuracy over previous techniques.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2005.11a
• /
• pp.415-424
• /
• 2005

The rapid growth of blog has caused information overload where bloggers in the virtual community space are no longer able to effectively choose the blogs they are exposed to. Recommender systems have been widely advocated as a way of coping with the problem of information overload in e-business environment. Collaborative Filtering (CF) is the most successful recommendation method to date and used in many of the recommender systems. Therefore, we propose a CF-based recommender system for bloggers in the virtual community space. Our proposed methodology consists of three main phases: In the first phase, we apply the "Interest Value" to a recommender system. The Interest Value is a quantity value about user preference in virtual community, and can measure the opinion of users accurately. Next phase, we generate the neighborhood group based on the Interest Value. In the final phase, we use the Community Likeness Score (CLS) to generate the top-n recommendation list. The methodology is explained step by step with an illustrative example and is verified with real data of a blog service provider.

• Journal of KIISE:Computer Systems and Theory
• /
• v.33 no.9
• /
• pp.650-657
• /
• 2006

The music is a part of our daily life in these days. And when the people listen to the music, they are affected by the context. However, previous researches on the music recommendation system have the problem that they didn't consider the proper contextual information efficiently. They only used the content-based filtering or the method to use musical metadata (genre, artist, etc.). Recently, there are some researches about the music recommendation system which applies the status(temperature, humidity, etc.) of environments. But, it is difficult to be accepted by the contextual information. Therefore, we propose the music recommendation system that is dynamically applied by the contextual information as well as the metadata in the previous researches. And the system can provide users with the music that they want to listen to, and then the users can be more satisfied. Also, the services can be improved by the feedback of the users. In order to solve this problem, the context-information for selecting a music list is defined and the music recommendation system is designed by using the content-based filtering method. The system is suitable for the user's taste and the context. The music recommendation system we are proposing uses an OSGi framework in the home network. As a result, the satisfaction of users and the quality of services will be improved more efficiently by supporting the mobility of services as well as the distributed processing.

• Journal of the Korean Society for information Management
• /
• v.26 no.1
• /
• pp.81-105
• /
• 2009

In this paper, we propose user contents using behavior and location information on contents on various channels, such as web, IPTV, for contents distribution. With methods to build user and contents profiles, contents using behavior as an implicit user feedback was applied into machine learning procedure for updating user profiles and contents preference. In machine learning procedure, contents-based and collaborative filtering methods were used to analyze user's contents preference. This study proposes contents location information on web sites for final recommendation contents as well. Finally, we refer to a generalized recommender system for personalization. With those methods, more effective and accurate recommendation service can be possible.

• Journal of Broadcast Engineering
• /
• v.18 no.1
• /
• pp.88-97
• /
• 2013

Collaborative filtering(CF) for the personalized recommendation is a successful and popular method in recommender systems. But the mainly researched and implemented cases focus on dealing with independent items with explicit feedback by users. For the domain of TV program recommendation in VOD service platform, we need to consider the unique characteristic and constraints of the domain. In this paper, we studied on the way to convert the viewing history of each TV program episodes to the TV program preference by considering the series structure of TV program. The former is implicit for personalized preference, but the latter tells quite explicitly about the persistent preference. Collaborative filtering is done by the unit of series while data gathering and final recommendation is done by the unit of episodes. As a result, we modified CF to make it more suitable for the domain of TV program VOD recommendation. Our experimental study shows that it is more precise in performance, yet more compact in calculation compared to the plain CF approaches. It can be combined with other existing CF techniques as an algorithm module.

• Journal of the Institute of Convergence Signal Processing
• /
• v.20 no.1
• /
• pp.31-36
• /
• 2019

A chatbot is an important area of mobile service, which understands informal data of a user as a conversational form and provides a customized service information for user. However, there is still a lack of a service way to fully understand the user's natural language typed query dialogue. Therefore, in this paper, we extract meaningful words, such a region, a food category, and a restaurant name from user's dialogue sentences for recommending a restaurant. and by comparing the extracted words against the contents of the knowledge database that is built from the hashtag for recommending a restaurant in SNS, and provides user target information having statistically much the word-similarity. In order to evaluate the performance of the restaurant recommendation chatbot system implemented in this paper, we measured the accessibility of various user query information by constructing a web-based mobile environment. As a results by comparing a previous similar system, our chabot is reduced by 37.2% and 73.3% with respect to the touch-count and the cutaway-count respectively.