• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.1
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• pp.43-52
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• 2013

Pervasive computing and social network are good resources in recommendation method. Collaborative filtering is one of the most popular recommendation methods, but it has some limitations such as rating sparsity. Moreover, it does not consider social network in pervasive computing environment. We propose an effective proactive friend recommendation method using social network and contexts in pervasive computing environment. In collaborative filtering method, users need to rate sufficient number of items. However, many users don't rate items sufficiently, because the rating information must be manually input into system. We solve the rating sparsity problem in the collaboration filtering method by using contexts. Our method considers both a static and a dynamic friendship using contexts and social network. It makes more effective recommendation. This paper describes a new friend recommendation method and then presents a music friend scenario. Our work will help e-commerce recommendation system using collaborative filtering and friend recommendation applications in social network services.

• Journal of KIISE
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• v.44 no.6
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• pp.621-627
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• 2017

Collaborative filtering is a technique that effectively recommends unrated items for users. Collaborative filtering is based on the similarity of the items evaluated by users. The existing top-N recommendation methods are based on pair-wise and list-wise preference models. However, these methods do not effectively represent the relative preference of items that are evaluated by users, and can not reflect the importance of each item. In this paper, we propose a new method to represent user's latent preference by combining an existing preference model and the notion of inverse user frequency. The proposed method improves the accuracy of existing methods by up to two times.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.803-811
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• 2010

This study analyzes the characteristics of preference ratings by dividing estimated values into four groups according to rank correlation coefficient after obtaining preference estimated value to user's ratings by using collaborative filtering algorithm. It is known that the value of standard error of skewness and standard error of kurtosis lower in the group of higher rank correlation coefficient This explains that the preference of higher rank correlation coefficient has lower extreme values and the differences of preference rating values. In addition, top n recommendation lists are made after obtaining rank fitting by using the result ranks of prediction value and the ranks of real rated values, and this top n is applied to the four groups. The value of top n recommendation is calculated higher in the group of higher rank correlation coefficient, and the recommendation accuracy in the group of higher rank correlation coefficient is higher than that in the group of lower rank correlation coefficient Thus, when using standard error of skewness and standard error of kurtosis in recommender system, rank correlation coefficient can be higher, and so the accuracy of recommendation prediction can be increased.

• Journal of Information Technology Applications and Management
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• v.26 no.6
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• pp.89-101
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• 2019

Many researchers have been focused on designing beauty product recommendation system for a long time because of increased need of customers for personalized and customized recommendation in beauty product domain. In addition, as the application of the deep neural network technique becomes active recently, various collaborative filtering techniques based on the deep neural network have been introduced. In this context, this study proposes a deep neural network model suitable for beauty product recommendation by applying Neural Collaborative Filtering and Generalized Matrix Factorization (NCF + GMF) to beauty product recommendation. This study also provides an implementation of web API system to commercialize the proposed recommendation model. The overall performance of the NCF + GMF model was the best when the beauty product recommendation problem was defined as the estimation rating score problem and the binary classification problem. The NCF + GMF model showed also high performance in the top N recommendation.

• Korean Journal of Soil Science and Fertilizer
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• v.30 no.1
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• pp.84-88
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• 1997

To establish N fertilizer recommendation method based on nitrate content of the soil for the Chinese cabbage grown in the plastic film house. Chinese cabbage was grown in the pots containing the plastic film house soils with various levels of $NO_3{^-}-N$ and different levels of fertilizer N. The nitrate nitrogen showed the positive correlation with nitrogen uptake amount by plant and the negative correlation with fertilizer nitrogen use efficiency of plant. The content of nitrate nitrogen in soil for maximum yield of Chinese cabbage was 310 mg/kg. An equation for the recommendation of fertilizer N for Chinese cabbage based on $NO_3{^-}-N$ in the soil was suggested.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.602-604
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• 2011

Due to wide usage of smartphones and mobile internet, mobile devices are used in various fields such as sending SMS, participating SNS, retrieving information and the number of users taking advantage of them are growing. The keypads of a mobile device are relatively smaller than those of desktop computers. Thus, the user has a difficulty in input sentences quickly and correctly. In this study, we introduce some string recommendation and modification techniques which can be used for helping a user input in mobile devices quickly and correctly. We describe a TRIE dictionary and n-gram language model which are the main technologies of the keyword recommendation applied to the online search engines.

• Korean Journal of Environmental Agriculture
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• v.31 no.3
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• pp.212-216
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• 2012

BACKGROUND: Green manure and graminaceousmanure crops have several benefits, such as improving soil physical and chemical properties and utilizing excessive greenhouse nutrients that they have a potential to be a water pollutant source. METHODS AND RESULTS: The objective of this study was to investigate nitrogen (N) supplying capabilities of green manure and graminaceous manure crops for lettuce (Lactuca sativa L.) and Chinese cabbage (Brassica campestris L.) grown under greenhouse conditions. For this two leguminous manures (Crotalaria juncea (Cr.) and Sesbaniaexaltata (Se.)) and two graminaceous manures (Sorghum bicolor; Haussolgo(Ha.) and Sudangrass (Sg.)) in the greenhouse were grown, cut, and incorporated into the greenhouse soil before planting. Chemical nitrogen (N) fertilizer rate was estimated based on N recommendation for lettuce and Chinese cabbage. 100% of the N recommended rates (1N) were 70 kg N $ha^{-1}$ for lettuce and 60 kg N $ha^{-1}$ for Chinese cabbage and 50% of the N recommendation rates (0.5N) were 35 kg N $ha^{-1}$ for lettuce and 30 kg N $ha^{-1}$ for Chinese cabbage. Nitrogen treatments were control (0N), Cr., Se., Cr + 0.5 N, Se + 0.5 N, Ha + 0.5 N, Sg + 0.5 N, and N recommendation rate (1N). Incorporated N from green manure and graminaceous manure crops were 130, 116, 93, and 87 kg N $ha^{-1}$ for Cr., Se., Ha., and Sg., respectively. Lettuce and Chinese cabbage were grown after incorporated green manure crops into the greenhouse soil. There was no significant difference in lettuce and Chinese cabbage yields under N treatments except control (0 kg/ha). Nitrogen use efficiency (NUE)was from 44% to 73% and the highest NUE was under Se. treatment. Although yields were not statistically different under N treatments except control, actual yield increase ranged from 170 to 1,100 kg/ha for lettuce and ranged from 2,770 to 5,210 kg/ha for Chinese cabbage compared to yield under N recommendation rate. Estimated economic benefit from this would be higher approximately between \2,770,000 and \5,210,000/ha under N treatments except control than the N recommendation rate. CONCLUSION: These results suggest that incorporating green manure crops, such as Cr. and SeSe. into soil or adding 0.5 N after incorporation of them can be beneficial in many ways in that it increases economic return because of yield increase, reduces the use of chemical N, and decreases the negative environmental impact on water quality because excessive N in the greenhouse soil can be used by green manure crops during the fallow.

• Journal of Korean Society for Quality Management
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• v.49 no.1
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• pp.17-29
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• 2021

Purpose: During 2015-2019, the average amount of political donation to the national assembly members in Korea was 1,000 won per person. Despite its benefits such as receiving tax credits, the donation system has not been actively practiced. This paper aims to promote political donations by suggesting a recommendation system of national assembly members by analysing the bills they proposed. Methods: In this paper, we propose a recommendation system based on two aspects: how similar the newly proposed or ammended bills are to the sponsors' interest (similarity index) and how much effort national assembly members put into those bills (intensity index). More than 25,000 bills were used to measure the recommendation quality index consisted with both the similarity and the intensity indices. Word2vec was used to calculate the similarity index of the bills proposed by the national assembly member to the sponsor's interest. The intensity index is calculated by diving the number of newly proposed or entirely revised bills with the number of senators who took part in those bills. Subsequently, we multiply the similarity index by the intensity index to obtain the recommendation quality index that can assist sponsors to identify potential assembly members for their donation. Results: We apply the proposed recommendation system to personas for illustration. The recommendation system showed an average f1 score about 0.69. The analysis results provide insights in recommendation for donation. Conclusion: n this study, the recommendation system was proposed to promote a political donation for national assembly members by creating the recommendation quality index based on the similarity and the intensity indices. We expect that the system presented in this paper will lower user barriers to political information, thereby boosting political sponsorship and increasing political participation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2381-2399
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• 2019

Traditional recommendation algorithms on Collaborative Filtering (CF) mainly focus on the rating prediction with explicit ratings, and cannot be applied to the top-N recommendation with implicit feedbacks. To tackle this problem, we propose a new collaborative filtering approach namely Maximize MAP with Matrix Factorization (MFMAP). In addition, in order to solve the problem of non-smoothing loss function in learning to rank (LTR) algorithm based on pairwise, we also propose a smooth MAP measure which can be easily implemented by standard optimization approaches. We perform experiments on three different datasets, and the experimental results show that the performance of MFMAP is significantly better than other recommendation approaches.

• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.1027-1038
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• 2006

There are many technical problems in the recommendation system based on very large database(VLDB). So, it is necessary to study the recommendation system' structure and the data-mining technique suitable for the large scale Internet shopping mail. Thus we design and implement the product recommendation system using k-means clustering algorithm and sequential pattern technique which can be used in large scale Internet shopping mall. This paper processes user information by batch processing, defines the various categories by hierarchical structure, and uses a sequential pattern mining technique for the search engine. For predictive modeling and experiment, we use the real data(user's interest and preference of given category) extracted from log file of the major Internet shopping mall in Korea during 30 days. And we define PRP(Predictive Recommend Precision), PRR(Predictive Recommend Recall), and PF1(Predictive Factor One-measure) for evaluation. In the result of experiments, the best recommendation time and the best learning time of our system are much as O(N) and the values of measures are very excellent.