• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.276-279
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• 2004

The location-based service is one of the most powerful services in the mobile area. The location-based service provides information service for moving user's location information and information service using wire / wireless communication. In this paper, we propose a model for personalized recommendation system which includes location information and personalized recommendation system for location-based service. For this service system, we consider mobile clients that have a limited resource and low bandwidth. Because it is difficult to input the words at mobile device, we must deliberate it when we design the interface of system. We design and implement the personalized recommendation system for location-based services(advertisement, discount news, and event information) that support user's needs and location information. As a result, it can be used to design the other location-based service systems related to user's location information in mobile environment. In this case, we need to establish formal definition of moving objects and their temporal pattern.

• 한국멀티미디어학회논문지
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• 제14권1호
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• pp.112-120
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• 2011

본 연구에서는 상황 적용 시스템을 이용하여 위치 인식 기반의 음식점 추천 서비스를 개발함으로써 이 시스템이 실제 상황 인식 응용 프로그램 개발에도 유용하게 사용될 수 있음을 보일 것이다. 이를 위해 상황 적응 시스템을 기반으로 하여 사용자의 위치 선호도와 검색 히스토리 등의 정보를 이용하는 위치 인식 기반의 맞춤형 음식점 추천 응용 프로그램을 개발하였다. 상황 적용 시스템은 개발자가 작성한 정책 파일의 내용에 따라 변화된 상황에 맞도록 응용 프로그램을 자동적으로 적응시키고, 응용 프로그램은 위치 등과 같은 변화된 상황을 기반으로 음식점 추천 서비스를 제공한다.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2003년도 Proceeding
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• pp.113-117
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• 2003

As the wireless communication technology advances rapidly, a personalization technology can be incorporated with the mobile Internet environment, which is based on location-based services to support more accurate personalized services. A location-based personalized recommender system is one of the essential technologies of the location-based application services, and is also a crucial technology for the ubiquitous environment. In this paper we propose a framework of a location-based personalized recommender system for the mobile Internet environment. The proposed system consists of three modules the interface module, the neighbor selection module and the prediction and recommendation module. The proposed system incorporates the concept of the recommendation system in the Electronic Commerce along with that of the mobile devices for possible expansion of services on the mobile devices. Finally a service scenario for entertainment recommendation based on the proposed recommender system is described.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권7호
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• pp.1868-1887
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• 2024

Recommendation systems research is a subfield of information retrieval, as these systems recommend appropriate items to users during their visits. Appropriate recommendation results will help users save time searching while increasing productivity at work, travel, or shopping. The problem becomes more difficult when the items are geographical locations on the ground, as they are associated with a wealth of contextual information, such as geographical location, opening time, and sequence of related locations. Furthermore, on social networking platforms that allow users to check in or express interest when visiting a specific location, their friends receive this signal by spreading the word on that online social network. Consideration should be given to relationship data extracted from online social networking platforms, as well as their impact on the geolocation recommendation process. In this study, we compare the similarity of geographic locations based on their distance on the ground and their correlation with users who have checked in at those locations. When calculating feature embeddings for users and locations, social relationships are also considered as attention signals. The similarity value between location and correlation between users will be exploited in the overall architecture of the recommendation model, which will employ graph convolution networks to generate recommendations with high precision and recall. The proposed model is implemented and executed on popular datasets, then compared to baseline models to assess its overall effectiveness.

• Asia pacific journal of information systems
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• 제16권4호
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• pp.239-254
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• 2006

Location-based advertising or application has been one of the drivers of third-generation mobile operators' marketing efforts in the past few years. As a result, many studies on location-based marketing or advertising have been proposed for recent several years. However, these approaches have two common shortcomings. First. most of them just suggested the theoretical architectures, which were too abstract to apply it to the real-world cases. Second, many of these approaches only consider service provider (seller) rather than customers (buyers). Thus, the prior approaches fit to the automated sales or advertising rather than the implementation of CRM. To mitigate these limitations, this study presents a novel advertisement recommendation model for mobile users. We call our model MAR-CF (Mobile Advertisement Recommender using Collaborative Filtering). Our proposed model is based on traditional CF algorithm, but we adopt the multi-dimensional personalization model to conventional CF for enabling location-based advertising for mobile users. Thus, MAR-CF is designed to make recommendation results for mobile users by considering location, time, and needs type. To validate the usefulness of our recommendation model. we collect the real-world data for mobile advertisements, and perform an empirical validation. Experimental results show that MAR-CF generates more accurate prediction results than other comparative models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권11호
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• pp.5344-5356
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• 2018

Nowadays with the help of Location-Based Social Networks (LBSNs), users of Point-of-Interest (POI) recommendation service in LBSNs are able to publish their geo-tagged information and physical locations in the form of sign-ups and share their experiences with friends on POI, which can help users to explore new areas and discover new points-of-interest, and promote advertisers to push mobile ads to target users. POI recommendation service in LBSNs is attracting more and more attention from all over the world. Due to the sparsity of users' activity history data set and the aggregation characteristics of sign-in area, conventional recommendation algorithms usually suffer from low accuracy. To address this problem, this paper proposes a new recommendation algorithm based on a novel Preference-Content-Region Model (PCRM). In this new algorithm, three kinds of information, that is, user's preferences, content of the Point-of-Interest and region of the user's activity are considered, helping users obtain ideal recommendation service everywhere. We demonstrate that our algorithm is more effective than existing algorithms through extensive experiments based on an open Eventbrite data set.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.352-368
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• 2021

Sign-in point of interest (POI) are extremely sparse in location-based social networks, hindering recommendation systems from capturing users' deep-level preferences. To solve this problem, we propose a content-aware POI recommendation algorithm based on a convolutional neural network. First, using convolutional neural networks to process comment text information, we model location POI and user latent factors. Subsequently, the objective function is constructed by fusing users' geographical information and obtaining the emotional category information. In addition, the objective function comprises matrix decomposition and maximisation of the probability objective function. Finally, we solve the objective function efficiently. The prediction rate and F1 value on the Instagram-NewYork dataset are 78.32% and 76.37%, respectively, and those on the Instagram-Chicago dataset are 85.16% and 83.29%, respectively. Comparative experiments show that the proposed method can obtain a higher precision rate than several other newer recommended methods.

• 한국컴퓨터정보학회논문지
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• 제17권5호
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• pp.81-91
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• 2012

최근 실내 위치기반서비스를 위한 다양한 측위 기술의 발전으로 실내에서도 사용자의 위치측정이 가능해짐에 따라 다양한 형태의 실내 위치기반 서비스가 개발되고 있다. 이에 쇼핑몰이나 백화점 등의 대규모 상업 공간 같은 복잡한 실내 공간에서 사용자에게 가장 적합한 위치나 매장을 추천하는 개인화된 POI 추천 시스템의 개발이 필요하게 되었다. POI 추천을 위해서는 사용자의 이동성과 대규모 상업공간의 공간성을 고려한 사용자 관심지점 탐사 기법의 연구가 필요하다. 이에 본 논문에서는 실내 위치기반 서비스의 POI 추천 시스템의 구현과 사용자들의 이동 데이터로부터 다양한 관심지점을 고려하기 위해 사용자가 일정 시간 동안 머무른 지점을 Stay point라 정의하고 실내공간에서 Stay point를 탐색하는 알고리즘을 제안하였다. 또한 제안된 알고리즘을 이용하여 탐색한 Stay point로부터 방문패턴을 탐사하여 POI 추천 시스템을 구현하였다. 구현된 시스템은 사용자의 모든 이동 로그를 이용한 패턴탐사보다 데이터양을 획기적으로 줄임으로써 빠른 패턴탐사와 메모리 사용량을 줄일 수 있었다.

• Journal of Information Processing Systems
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• 제19권5호
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• pp.576-589
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• 2023

To upgrade home style recommendations and user satisfaction, this paper proposes a personalized and optimized recommendation algorithm for interior design style based on local social network, which includes data acquisition by three-dimensional (3D) model, home-style feature definition, and style association mining. Through the analysis of user behaviors, the user interest model is established accordingly. Combined with the location-based social network of association rule mining algorithm, the association analysis of the 3D model dataset of interior design style is carried out, so as to get relevant home-style recommendations. The experimental results show that the proposed algorithm can complete effective analysis of 3D interior home style with the recommendation accuracy of 82% and the recommendation time of 1.1 minutes, which indicates excellent application effect.

• 정보관리학회지
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• 제26권1호
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• pp.81-105
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• 2009

본 연구에서는 웹, IPTV 등의 콘텐츠 유통망에서의 개인화 추천서비스를 위하여 이용자의 콘텐츠 이용행위와 콘텐츠의 위치정보를 활용한 추천방법을 제안하고 있다. 추천방법의 성능향상을 위하여 이용자 및 콘텐츠 프로파일 생성방법과 함께, 이용자의 콘텐츠 이용행위를 암묵적 이용자 피드백으로서 학습과정에 적용하여 이용자 선호도를 분석하였다. 학습과정에서의 이용자 선호도 분석을 위하여 협업여과추천방법 및 내용기반추천 방법을 적용하였다. 또한 보다 정확한 추천을 위한 최종 콘텐츠 추천을 위하여 웹사이트 상의 콘텐츠에 대한 위치정보를 활용한 추천방법을 제안하고 있다. 이를 통하여 보다 효율적이고 정확한 추천 서비스의 제공이 가능할 수 있다.