• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.10 no.3
• /
• pp.51-59
• /
• 2010

There have been proposed several movie recommendation algorithms based on Collaborative Filtering(CF). CF decides neighbors whose ratings are the most similar to each other and it predicts how well users will like new movies, based on ratings from neighbors. This paper proposes a new method to improve the result predicted by CF based on genres of the movies seen by users. The proposed method can be combined to the most of all existing CF algorithms. In this paper, a performance evaluation has been conducted between an existing simple CF algorithm and CF-Genre that is the proposed genre-based method added to the CF algorithm. The result shows that CF-Genre improves 3.3% in prediction performance over existing CF algorithms.

• Smart Media Journal
• /
• v.5 no.2
• /
• pp.84-89
• /
• 2016

For a place-recommendation model based on user's behavior and multi-attribute attitude in this thesis. We focus groups that show similar patterns of visiting restaurants and then compare one and the other. We make use of The Fishbein Equation, Pearson's Correlation Coefficient to calculate multi-attribute attitude scores. Furthermore, We also make use of Preference Prediction Algorithm and Distance based method named "Euclidean Distance" to provide accurate results. We can demonstrate how excellent this system is through several experiments carried out with actual data.

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

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.5
• /
• pp.171-176
• /
• 2022

With the recent development of deep learning technology, the areas of recommendation systems have also diversified. This paper studied algorithms to improve the learning rate and studied the significance results according to words through comparison with the performance characteristics of the Word2Vec model. The problem recommendation algorithm was implemented with the values expressed through the reflection of meaning and similarity test between texts, which are characteristics of the Word2Vec model. Through Word2Vec's learning results, problem recommendations were conducted using text similarity values, and problems with high similarity can be recommended. In the experimental process, it was seen that the accuracy decreased with the quantitative amount of data, and it was confirmed that the larger the amount of data in the data set, the higher the accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.10a
• /
• pp.207-209
• /
• 2022

In the IoT environment, it is possible to collect life pattern data by recognizing human physical activity from smart devices. In this paper, the proposed model consists of a prediction stage and a recommendation stage. The prediction stage predicts the scale of anxiety and depression by using logistic regression and k-nearest neighbor algorithm through machine learning on the dataset collected from life pattern data. In the recommendation step, if the symptoms of anxiety and depression are classified, the principal component analysis algorithm is applied to recommend food and light exercise that can improve them. It is expected that the proposed anxiety/depression prediction and food/exercise recommendations will have a ripple effect on improving the quality of life of individuals.

• The Journal of Society for e-Business Studies
• /
• v.20 no.2
• /
• pp.155-166
• /
• 2015

RWR (Random Walk with Restart) is frequently used by many graph-based ranking algorithms, but it does not consider a signed graph where edges may have negative weight values. In this paper, we apply the Balance Theory by F. Heider to RWR over a signed graph and propose a novel RWR, Balanced Random Walk (BRW). We apply the proposed technique into the domain of recommendation system, and show by experiments its effectiveness to filter out the items that users may dislike. In order to provide the reasonable performance of BRW in the domain, we modify the existing Top-k algorithm, BCA, and propose a new algorithm, Bicolor-BCA. The proposed algorithm yet requires employing a threshold. In the experiment, we show how threshold values affect both precision and performance of the algorithm.

• Journal of Korea Multimedia Society
• /
• v.22 no.9
• /
• pp.1108-1121
• /
• 2019

As the quality and level of life rise, many people are doing search for various pieces of information about tourism. In addition, users prefer the search methods reflecting individual opinions such as SNS and blogs to the official websites of tourist destination. Many of previous studies focused on a recommendation system for tourist courses based on the GPS information and past travel records of users, but such a system was not capable of recommending the latest tourist trends. This study thus set out to collect and analyze the latest SNS data to recommend tourist destination of high interest among users. It also aimed to propose an altered TSP algorithm to recommend the optimal routes to the recommended destination within an area and a system to recommend the optimal tourist courses by applying the Elasticsearch engine. The altered TSP algorithm proposed in the study used the location information of users instead of Dijkstra's algorithm technique used in previous studies to select a certain tourist destination and allowed users to check the recommended courses for the entire tourist destination within an area, thus offering more diverse tourist destination recommendations than previous studies.

• Proceedings of the IEEK Conference
• /
• 2001.06b
• /
• pp.305-308
• /
• 2001

This paper describes the design of elliptic curve cryptographic (ECC) coprocessor over binary fields for the If card. This coprocessor is implemented by the shift-and-add algorithm for the field multiplication algorithm. And the modified almost inverse algorithm(MAIA) is selected for the inverse multiplication algorithm. These two algorithms is merged to minimize the hardware size. Scalar multiplication is performed by the binary Non Adjacent Format(NAF) method. The ECC we have implemented is defined over the field GF(2$^{163}$), which is a SEC-2 recommendation［7］..

• Journal of Internet Computing and Services
• /
• v.8 no.5
• /
• pp.81-89
• /
• 2007

In this thesis, we propose ontology based context-aware recommendation system using concept hierarchy(OCARCH), Context-aware recommendation services are useful to provide an user with relevant information and/or services bared on his current context, However several approaches to context-aware recommendation system have been already proposed, each of them provide information without considering level of information concept bared on his current context, For this reason, we propose OCARCH as system capable of helping people to find their way quickly and easily through large amounts of information by determining level of information concept based on his current context, We are also using prefetching algorithm to store recommendation information that the user is likely to need in the near future based on current predictions, Therefore the OCARCH enables users to obtain relevant information efficiently, Several experiments are performed and the experimental results show that the proposed system provides more effective than conventional context-aware recommendation system.

• Journal of Broadcast Engineering
• /
• v.24 no.6
• /
• pp.1013-1023
• /
• 2019

In content consumption environment with various live TV channels, VoD contents and web contents, recommendation service is now a necessity, not an option. Currently, various kinds of recommendation services are provided in the OTT service or the IPTV service, such as recommending popular contents or recommending related contents which similar to the content watched by the user. However, in the case of a content viewing environment through TV or IPTV which shares one TV and a TV set-top box, it is difficult to recommend proper content to a specific user because one or more usage histories are accumulated in one subscription information. To solve this problem, this paper interprets the concept of family as {user, time}, extends the existing recommendation relationship defined as {user, content} to {user, time, content} and proposes a method based on deep learning algorithm. Through the proposed method, we evaluate the recommendation performance qualitatively and quantitatively, and verify that our proposed model is improved in recommendation accuracy compared with the conventional method.