• 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.

• Proceedings of the Korea Society of Design Studies Conference
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• 2000.11a
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• pp.114-115
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• 2000

IMF 한파와 더불어 건설 시장 역시 침체되어 있는 되도 불구하고 사회 및 문화의 변화속도는 빨라지고 있으며 소비자들의 주택 선호도 역시 다양해지고 있다. 과거 정부주도의 주택 확대정책에 따른 획일화된 아파트의 양적 팽창은 이러한 빠른 변화와 다양화에 적응할 수 없는게 현실이며 또한 많은 문제점을 남기게 되었다. (중략)

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.156-163
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• 2006

CF systems are widely used in recommendation due to the easy implementation and the outstanding performance. They have several problems such as the sparsity problem, the first-rater problem, and recommending explanation. Many studies are suggested to resolve these problems. While the influence of the sparsity problem lessens as the users' data are accumulated, but the first-rater problem is originated from the CF systems and there are a number of researches to overcome the disadvantages of CF systems based on the content-based methods. Also CF systems are black boxes, providing no explanation of working of the recommendation. In this paper we present a content-based prediction system based on the preference words, which exposes the reasoning behind a recommendation. Our system predicts user's rating of a new movie and we suggest a semiotic network-based method to solve the mismatching problem between the items. For experimental comparison, we used EachMovie and IMDb dataset.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.8
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• pp.74-84
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• 2010

In this paper, we propose a preference-based shortest path algorithm which is combined with Ant Colony Optimization (ACO) and A* heuristic algorithm. In recent years, with the development of ITS (Intelligent Transportation Systems), there has been a resurgence of interest in a shortest path search algorithm for use in car navigation systems. Most of the shortest path search algorithms such as Dijkstra and A* aim at finding the distance or time shortest paths. However, the shortest path is not always an optimum path for the drivers who prefer choosing a less short, but more reliable or flexible path. For this reason, we propose a preference-based shortest path search algorithm which uses the properties of the links of the map. The preferences of the links are specified by the user of the car navigation system. The proposed algorithm was implemented in C and experiments were performed upon the map that includes 64 nodes with 118 links. The experimental results show that the proposed algorithm is suitable to find preference-based shortest paths as well as distance shortest paths.

• Journal of KIISE:Software and Applications
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• v.29 no.9
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• pp.669-675
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• 2002

In recent years most of personalized recommender systems in electronic commerce utilize collaborative filtering algorithm in order to recommend more appropriate items. User-based collaborative filtering is based on the ratings of other users who have similar preferences to a user in order to predict the rating of an item that the user hasn't seen yet. This nay decrease the accuracy of prediction because the similarity between two users is computed with respect to the two users and only when an item has been rated by the users. In item-based collaborative filtering, the preference of an item is predicted based on the similarity between the item and each of other items that have rated by users. This method, however, uses the ratings of users who are not the neighbors of a user for computing the similarity between a pair of items. Hence item-based collaborative filtering may degrade the accuracy of a recommender system. In this paper, we present a new approach that a user's neighborhood is used when we compute the similarity between the items in traditional item-based collaborative filtering in order to compensate the weak points of the current item-based collaborative filtering and to improve the prediction accuracy. We empirically evaluate the accuracy of our approach to compare with several different collaborative filtering approaches using the EachMovie collaborative filtering data set. The experimental results show that our approach provides better quality in prediction and recommendation list than other collaborative filtering approaches.

• Journal of the Korea Computer Industry Society
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• v.8 no.2
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• pp.89-96
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• 2007

The object-based reservation scheduling techniques are to solve complex scheduling problems using constraint satisfaction problems and object-oriented concepts. We have tried to apply the object-based reservation scheduling techniques to the flight operation scheduling problems. For crew's satisfaction, we have considered the total crew's preferences board in the flight operation scheduling. To consider the over all satisfaction, the events of every object are alloted to the board along its priority. Constraints to reservation scheduling are classified to global and local. The definition of board and information of every event are global constraints and the preferences to object's board slots are local constraints. Actually, we have made an experiment on flight operation scheduling in order to raise crew's satisfaction.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.431-434
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• 2012

시간 흐름에 따라 TV 프로그램 스케줄은 변화하고 스케줄의 변화는 사용자 선호에 영향을 미친다. 이러한 스케줄 변화에 따른 토픽의 흐름이 사용자 선호도에 미치는 영향 외에도, 개성에 따른 선호도의 변화는 개인별 차이가 크다. 본 논문은 사용자 선호도 변화에 적응적으로 대응하면서 시간 변화에도 일정한 관심을 보이는 사용자의 선호도에는 가중치를 더한 모델을 목표로 한다. 따라서 제안 모델은 현재의 시청 데이터를 기준으로 한 사용자별 선호도의 선행 정보(prior)로 이전 시청선호를 두었고, 선호도 변화와 일관성을 고려하여 하나의 시청길이에 대한 선호도뿐만 아니라 여러 시청 길이의 선호도를 결합한 선호도를 구성할 수 있는 확장성 있는 모델을 제시한다. 선호도의 일관성에 대한 가중치 연산에 있어 전체 확률모델의 확률을 향상시키는 연산을 통해 정교성을 더한 모델을 제시한다. 실제 사용자들이 시청한 데이터인 2011 TNMS데이터를 기준으로 제안 모델의 성능을 확인한 결과, 기존의 LDA, MDTM모델 보다 나은 성능을 보임을 확인할 수 있었으며, 1주일 단위 추천결과, 5개 추천 시, 최대 67.9%의 추천 정확도를 확인할 수 있었다.

• Journal of Korea Multimedia Society
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• v.10 no.10
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• pp.1356-1366
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• 2007

Many personalized Services that provide users with adaptive information according to users' requirements and preferences have been researched and developed. However, existing approaches are difficult to share a user's information among heterogeneous services because these approaches manage users' preferences in a single system. In this paper, we propose a user preference sharing model based on the Semantic Web as a solution to resolve the problem. Our model enables user preferences to be described and shared over service-specific ontologies which are affected by the feature of each service. Our model is analyzed and evaluated with an implementation of the middleware that supports our model. Our approach has the advantage of providing more efficient personalized services than existing approaches because it can describe users' preferences centering around each service and share these information among heterogeneous personalized services.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.85-94
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• 2009

It is becoming increasingly difficult for learners to find the lectures they are looking for. In turn, the ability to find the particular lecture sought by the learner in an accurate and prompt manner has become an important issue in e-Learning. To deal this issue, in this paper. we present a collaborative approach to provide personalized recommendations of online video lectures. The proposed approach first identifies candidated video lectures that will be of interest to a certain user. Partitioned collaborative filtering is employed as an approach in order to generate neighbor learners and predict learners'preferences for the lectures. Thereafter, Attribute-based filtering is employed to recommend a final list of video lectures that the target user will like the most.

• The KIPS Transactions:PartC
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• v.13C no.5 s.108
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• pp.651-658
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• 2006

Mobile computing environment is known to be quite difficult to provide user with a stable QoS(Qualify of Service) due to its mobility nature. In order to protect the inherent characteristics of wireless network such as low bandwidth and high transmission delay along with the user's mobility, many works are conducted to apply caching and prefetching methods. This paper presents a novel prefetching technique which is based on user's preference, that is, interest and Uuかity. It tries to improves the effectiveness of prefetching by separating and appling the interest with personal tendency of a given information, and the popularity with general tendency of the information. The proposed scheme shown relatively superior performance in terms of the utilization ratio of prefetched information and the failure ratio of information retrieval than the existing methods.