• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.23-28
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• 2022

Recently smart device(such as smart phone and tablet PC) become a role as an information gateway, using of the web news by multiple users from the web portal has been more important things. However, the quantity of creating web news on the web makes hard to catch the information which the user wants and confuse the users cause of the similar and repeated contents. In this paper, we propose the news recommend system using the document summarization based on KoBART which gives the selected news to users from the candidate news on the news portal. As a result, our proposed system shows higher performance and recommending the news efficiently by pre-training and fine-tuning the KoBART using collected news data.

• Asia Marketing Journal
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• v.17 no.4
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• pp.43-61
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• 2016

This paper has identified some theoretical reasons and empirical evidence for negative scores that occur in Japan and Korea or unstable NPS scores that can be experienced. A psychological analysis of NPS results sheds light on the validity of the negative NPS scores that are often found in Japan and Korea. Usually customer experience surveys utilize a "single stimulus" such as the "company" or the "company's products / services." However, in the case of the "recommendation to friend" question of the NPS system there are two stimuli namely the "company product/service" and the influence of "friends." Hence, the survey outcomes from this question can be very different when compared with other single stimulus questions such as "overall satisfaction" or "repurchase." Japanese and Korean people may have a positive attitude towards the company but they will provide low NPS scores because they are reflecting that they would not run the risk of ruining their relationships with their friends by making a recommendation. As a result, in the NPS system these people will be labeled as "detractors" when in fact they are "ambivalent customers." Using several Japanese and Korean based marketing research industry examples and case studies, different strategies are proposed to address the issue of negative scores in the NPS system in Japan and Korea. The Customers Psyche appears to be the key determinant factors for both types of behavioural items (items with a single stimulus as well as items with two stimuli).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.392-394
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• 2021

This paper is a thesis on an automatic restaurant recommendation application for tourists traveling to travel destinations. when you run the application at any travel destination in KOREA, it is an application that recommends desired services such as Korean, Chinese, Western, etc, regardless of the type of food, so that restaurant rankings are poured out in tourist destinations. not only recommending restaurants, but also collecting related information DB so that you can easily find restaurants in tourist destinations through reviews and stars such as hygiene conditions, prices, and compliance with quarantine regulations due to the recent coronavirus. the application was developed

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.7
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• pp.1355-1362
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• 2016

Recently, the marine leisure are being emphasized with improving the quality of life style by increased income and spare time. Also, there is a increasement of people's interest in marine leisure activities. But resources and facilities do not grow in proportion to the quantitative growth of the current marine leisure industry. Besides, a leisure ship operator tends to choose a simple or familiar route of the local area rather than a new leisure routes which are not explored due to lack of accessible areas information. This paper proposes a routes recommendation system in order to solve above problems based on marine resource database. The databases have been constructed through investigation and analysis of navigational information such as environmental conditions including weather conditions and sea status, field of marine leisure activities, tourist attractions and natural landscape, and marine leisure prohibited areas. Therefore we have developed and implemented the route recommendation system that provides various information necessary to route operation of leisure boats.

• Journal of Internet Computing and Services
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• v.17 no.5
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• pp.75-87
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• 2016

With the exploration of digital academic information, it is certainly required to develop more effective academic contents recommender system in order to accommodate increasing needs for accessing more personalized academic contents. Considering historical usage data, the academic content recommender system recommends personalized academic contents which corresponds with each user's preference. So, the academic content recommender system effectively increases not only the accessibility but also usability of digital academic contents. In this paper, we propose the new journal recommendation technique based on information of journal subscription and web usage logs in order to properly recommend more personalized academic contents. Our proposed recommendation method predicts user's preference with the institution similarity, the journal similarity and journal importance based on citation relationship data of references and finally compose institute-oriented recommendations. Also, we develop a recommender system prototype. Our developed recommender system efficiently collects usage logs from distributed web sites and processes collected data which are proper to be used in proposed recommender technique. We conduct compare performance analysis between existing recommender techniques. Through the performance analysis, we know that our proposed technique is superior to existing recommender methods.

• Journal of Intelligence and Information Systems
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• v.15 no.1
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• pp.123-140
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• 2009

Collaborative filtering recommends products using customers' preferences, so it cannot recommend products to the new customer who has no preference information. This paper proposes a novel approach to new customer recommendations using the social network analysis which is used to search relationships among social entities such as genetics network, traffic network, organization network, etc. The proposed recommendation method identifies customers most likely to be neighbors to the new customer using the centrality theory in social network analysis and recommends products those customers have liked in the past. The procedure of our method is divided into four phases : purchase similarity analysis, social network construction, centrality-based neighborhood formation, and recommendation generation. To evaluate the effectiveness of our approach, we have conducted several experiments using a data set from a department store in Korea. Our method was compared with the best-seller-based method that uses the best-seller list to generate recommendations for the new customer. The experimental results show that our approach significantly outperforms the best-seller-based method as measured by F1-measure.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.147-168
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• 2011

As personalized recommendation of products and services is rapidly growing in importance, a number of studies provided fundamental knowledge and techniques for developing recommendation systems. Among them, the CF technique has been most widely used and has proven to be useful in many practices. However, current collaborative filtering (CF) technique has still considerable rooms for improving the effectiveness of recommendation systems: 1) a similarity function most systems use to find so-called like-minded people is not well defined in that similarity is computed from a single perspective of similarity concept; and 2) temporal information that contains the changing preference of customers needs to be taken into account when making recommendations. We hypothesize that integration of multiple aspects of similarity and utilization of temporal information will improve the accuracy of recommendations. The objective of this paper is to test the hypothesis through a series of experiments using MovieLens data. The experimental results show that the proposed recommendation system highly outperforms the conventional CF-based systems, confirming our hypothesis.

• Journal of the Korea Society of Computer and Information
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• v.18 no.10
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• pp.23-37
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• 2013

In this paper, the proposed movie recommender system constructs trust network, which is similar to social network, using user's trust information that users explicitly present. Recommendation on items is performed by using relation degree between users and information of recommended item is provided by a visualization method. We discover the hidden relationships via the constructed trust network. To provide visualized recommendation information, we employ Freebase which is large knowledge base supporting information such as movie, music, and people in structured format. We provide three visualization methods as the followings: i) visualization based on movie posters with the number of movies that user required. ii) visualization on extra information such as director, actor and genre and so on when user selected a movie from recommendation list. iii) visualization based on movie posters that is recommended by neighbors who a user selects from trust network. The proposed system considers user's social relations and provides visualization which can reflect user's requirements. Using the visualization methods, user can reach right decision making on items. Furthermore, the proposed system reflects the user's opinion through recommendation visualization methods and can provide rich information to users through LOD(Linked Open Data) Cloud such as Freebase, LinkedMDB and Wikipedia and so on.

• KIISE Transactions on Computing Practices
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• v.22 no.8
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• pp.375-380
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• 2016

The proliferation of online social networking services (OSNSs) and smartphones has enabled people to easily make friends with a large number of users in the online communities, and interact with each other. This leads to an increase in the usage rate of OSNSs. However, individuals who have immersed into their digital lives, prioritizing the virtual world against the real one, become more and more isolated in the physical world. Thus, their socialization processes that are undertaken only through lots of face-to-face interactions and trial-and-errors are apt to be neglected via 'Add Friend' kind of functions in OSNSs. In this paper, we present a friend recommendation system based on the on/off-line contextual information for the OSNS users to have more serendipitous offline interactions. In order to accomplish this, we modeled both offline information (i.e., place visit history) collected from a user's smartphone on a 3D tensor, and online social data (i.e., friend relationships) from Facebook on a matrix. We then recommended like-minded people and encouraged their offline interactions. We evaluated the users' satisfaction based on a real-world dataset collected from 43 users (12 on-campus users and 31 users randomly selected from Facebook friends of on-campus users).

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.674-677
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• 2002

This paper describes the design of elliptic curve crypto (ECC) coprocessor over binary fields for ECC protocols. Our ECC processor provides the elliptic curve operations for Diffie-Hellman, EC Elgamal and ECDSA protocols. The ECC we have implemented is defined over the fieTd GF(2$\^$163/),which is a SEC-2 recommendation［6］.