• Journal of Digital Convergence
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• v.12 no.6
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• pp.277-287
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• 2014

In LBSNS(Location-based Social Network Service), users can share locations and communicate with others by using check-in data. The check-in data consists of POI name, category, coordinate and address of locations, nickname of users, evaluating grade of locations, related article/photo/video, and etc. If you analyze the check-in data from the location-based social network service in accordance with your situation, you can provide various customized services. Therefore, In this paper, we develop a location recommendation system based on LBSNS that can utilize the check-in data efficiently. This system analyzes the location category of the check-in data, determines the weighted value of it, and finds out the similarity between users by using the Pearson correlation coefficient. Also, it obtains the preference score of recommended locations by using the collaborated filtering algorithm and then, finds out the distance score by applying the Euclidean's algorithm to the recommended locations and the current users' locations. Finally, it recommends appropriate locations by applying the weighted value to the preference score and the distance score. In addition, this paper approved excellence of the proposed system throughout the experiment using real data.

• Proceedings of the Korea Contents Association Conference
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• 2017.05a
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• pp.17-18
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• 2017

손쉽게 현 위치 정보를 공유하고 사용자 간 커뮤니케이션이 가능한 위치 기반 소셜 네트워크가 대중화되면서 장소 추천에 대한 연구가 활발히 진행되어 있다. 본 논문은 시간대별 사용자 선호도와 주요 활동 영역을 고려한 POI 추천 기법을 제안한다. 장소 카테고리별 사용자의 체크인(che-ck-in)정보를 시간대로 분할하여 시간에 따른 장소의 선호도를 판별하고 사용자의 과거 이력을 이용하여 사용자별 활동 영역을 선별한다. 장소의 선호도와 선별된 활동 영역에 기반하여 협업 필터링을 수행하여 POI를 추천한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.306-307
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• 2023

본 연구는 협업 기반 필터링을 이용하여 반려동물 동반 가능 장소를 추천해주는 시스템을 제안한다. 반려동물 양육 인구가 늘고 있는 현재에 반해 반려동물을 대상으로 하는 추천 시스템은 발전이 더딘 상황이다. 반려동물은 다양한 크기와 종류를 갖고 있기 때문에 기존의 인간 기준의 추천 시스템과는 다르게 접근해야 할 필요성이 있다. 본 연구에서는 반려동물의 다양한 특성을 고려한 장소를 추천해주기 위해 협업 기반 필터링을 활용하였다. 사용자 데이터의 수가 늘어나면 결과의 정확도를 높여주지만, 사용자 간의 유사도를 구하는 비용이 증가한다. 이러한 장단점을 고려하여 '아이템 기반 협업 필터링' 과 '사용자 기반 협업 필터링' 방법을 적절히 사용하는 방향을 제안한다.

• The Journal of the Korea Contents Association
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• v.18 no.1
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• pp.64-75
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• 2018

With the development of location-aware technologies and the activation of smart phones, location based social networks(LBSN) have been activated to allow people to easily share their location. In particular, studies on recommending the location of user interests by using the user check-in function in LBSN have been actively conducted. In this paper, we propose a location recommendation scheme considering time and user activities in LBSN. The proposed scheme considers user preference changes over time, local experts, and user interest in rare places. In other words, it uses the check-in history over time and distinguishes the user activity area to identify local experts. It also considers a rare place to give a weight to the user preferred place. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.23-31
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• 2020

The need for personalized recommendation is growing due to convenient access and various types of items due to the development of information communication and smartphones. Weather and weather conditions have a great influence on the decision-making of users' places and activities. This weather information can increase users' satisfaction with recommendations. In this paper, we propose a collaborative filtering-based place recommendation system using living index by utilizing living index of users' location information on mobile platform to find users with similar propensity and to recommend places by predicting preferences for places. The proposed system consists of a weather module for analyzing and classifying users' weather, a recommendation module using collaborative filtering for place recommendations, and a management module for user preferences and post-management. Experiments have shown that the proposed system is valid in terms of the convergence of collaborative filtering algorithms and living indices and reflecting individual propensity.

• KIISE Transactions on Computing Practices
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• v.21 no.6
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• pp.403-411
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• 2015

Nowadays, in the so-called 'smart era', people are likely to feel more comfortable in on-line meetings than in off-line meetings. However, on-line meetings are often considered unimportant and it is difficult for participants to share their feelings. This paper suggests a mobile application that can revitalize off-line meetings to address these problems. Wecok Application, which suggests the best meeting place by applying users' preferences and their locations, provides a function-oriented user interface and simple touch flow. Wecok consists of a client/server software, and currently supports only three users simultaneously. It enables exchange of off-line and on-line communication by expanding meetings from on-line to off-line. By using Wecok, users can easily decide on an off-line meeting place.

• Journal of Digital Contents Society
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• v.18 no.3
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• pp.535-542
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• 2017

The ubiquitous computing made it happen to easily take cognizance of context, which includes user's location, status, behavior patterns and surrounding places. And it allows providing the catered service, designed to improve the quality and the interaction between the provider and its customers. The personalized recommendation service needs to obtain logical reasoning to interpret the context information based on user's interests. We researched a model that connects to the practical value to users for their daily life; information about restaurants, based on several mobile contexts that conveys the weather, time, day and location information. We also have made various approaches including the accurate rating data review, the equation of Naïve Bayes to infer user's behavior-patterns, and the recommendable places pre-selected by preference predictive algorithm. This paper joins a vibrant conversation to demonstrate the excellence of this approach that may prevail other previous rating method systems.

• The Journal of Society for e-Business Studies
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• v.21 no.3
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• pp.15-28
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• 2016

This paper proposes a system recommending spatial information what user wants with collecting and analyzing tweets around the user's location by using the GPS information acquired in mobile. This system has built an emotion dictionary and then derive the recommendation score of morphological analyzed tweets to provide not just simple information but recommendation through the emotion analysis information. The system also calculates distance between the recommended tweets and user's latitude-longitude coordinates and the results showed the close order. This paper evaluates the result of the emotion analysis in a total of 10 areas with two keyword 'Restaurants' and 'Performance.' In the result, the number of tweets containing the words positive or negative are 122 of the total 210. In addition, 65 tweets classified as positive or negative by analyzing emotions after a morphological analysis and only 46 tweets contained the meaning of the positive or negative actually. This result shows the system detected tweets containing the emotional element with recall of 38% and performed emotion analysis with precision of 71%.

• Convergence Security Journal
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• v.19 no.2
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• pp.73-81
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• 2019

In this paper, we propose a user-dependent mid-point navigation system using a time weighted mid-point navigation algorithm and a user preference based mid-point neighborhood recommendation system. The proposed system consists of a mid-point navigation module for calculating an mid-point by applying a time weight of each user based on a departure point between users, and a search module for providing a search for a route to the calculated mid-point. In addition, based on the mid-point search result, it is possible to increase the utilization rate of users by including a place recommending function based on user's preference. Experimental results show that the proposed system can increase the efficiency of using by the user-dependent mid-point navigation and place recommendation function.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.16-18
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• 2023

본 연구는 궤적 데이터(trajectory data)를 대상으로 증강 그래프 기반의 그래프 뉴럴 네트워크를 활용하여 다음에 방문한 장소를 추천하는 모델을 제안한다. 제안 모델은 전체 궤적 데이터를 그래프로 표현하여 추출한 글로벌 궤적 플로우의 특성을 다음 방문할 POI 추천에 활용한다. 이때, POI 추천시 자주 발생하는 두 가지 문제를 추가로 해결함으로써 POI 추천의 정확도를 높이는 것을 목표로 한다. 첫 번째 문제는 추천 대상 궤적 데이터의 길이가 짧은 경우에 성능 저하가 발생한다는 것이다. 두 번째 문제는 콜드-스타트 문제이다. 기존 POI 추천 모델은 매우 적은 방문 기록만 가지는 사용자 또는 POI에 대해서는 매우 낮은 예측 성능을 보인다. 본 연구에서는 궤적 그래프에서 일부 엣지를 삭제하여 생성한 증강 그래프 기반의 궤적 플로우 특징 기반 모델을 제안함으로써 짧은 길이의 궤적 데이터 및 콜드-스타트 사용자/POI에 대한 추천 성능을 높인다.