• 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 Biosystems Engineering
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• v.42 no.2
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• pp.98-103
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• 2017

Purpose: Precision agriculture includes determining the right amount of nitrogen for a specific location in the field. This work focused on developing and validating a model using variable rate nitrogen application based on the estimated SPAD value from the ground-based image sensor. Methods: A variable rate N application based on the decision making system was performed using a sensor-based variable rate nitrogen application system. To validate the nitrogen application decision making system based on the SPAD values, the developed N recommendation was compared with another conventional N recommendation. Results: Sensor-based variable rate nitrogen application was performed. The nitrogen deficiency level was measured using the image sensor system. Then, a variable rate application was run using the decision model and real-ti me control. Conclusions: These results would be useful for nitrogen management of corn in the field. The developed nitrogen application decision making system worked well, when considering the SPAD value estimation.

• 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.

• Korean Journal of Soil Science and Fertilizer
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• v.34 no.1
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• pp.64-70
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• 2001

To verify the newly developed decision assisting expert system for nitrogen fertilizer application NES to Illinois cornfields, a couple of N rate studies from Dr. Howard and five Illinois Agricultural Experiment Stations were applied. Four types of recommendations including the current Illinois recommendation, Hoeft recommendation, NES, and maximum economic recommendation were compared with each other for the crop yields, profits, recovery rate, and N losses to cornfields. The N rate of NES recommendation, considering productivity index (PI), soil organic matter content (SOM), and pre-sidedressing nitrate concentration (PSNT) level, was the lowest in comparison to those of other recommendations. However, N recovery rate in NES was generally higher and the resulting N loss was lower than others. But, adherence to the recommendations may also reduce farmers income if environmental expense did not considered. Therefore, NES will be more effective by adding the factors including environmental expense, tillage systems, crop rotation, and other agricultural management parameters.

• Journal of Crop Science and Biotechnology
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• v.10 no.1
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• pp.33-38
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• 2007

Nitrogen management at the panicle initiation stage(PI) should be fine-tuned for securing a concurrent high yield and high quality rice production. For calibration and testing of the recommendation models of N topdressing rates at PI for target grain yield and protein content of rice, three split-split-plot design experiments including five rice cultivars and various N rates were conducted at the experimental farm of Seoul National University, Korea from 2003 to 2005. Data from the first two years of experiments were used to calibrate models to predict grain yield and milled-rice protein content using shoot fresh weight(FW), chlorophyll meter value(SPAD), and the N topdressing rate(Npi) at PI by stepwise multiple regression. The calibrated models explained 85 and 87% of the variation in grain yield and protein content, respectively. The calibrated models were used to recommend Npi for the target protein content of 6.8%, with FW and SPAD measured for each plot in 2005. The recommended N rate treatment was characterized by an average protein content of 6.74%(similar to the target protein content), reduced the coefficient of variation in protein content to 2.5%(compared to 4.6% of the fixed rate treatment), and increased grain yield. In the recommended N rate treatments for the target protein content of 6.8%, grain yield was highly dependent on FW and SPAD at PI. In conclusion, the models for N topdressing rate recommendation at PI were successful under present experimental conditions. However, additional testing under more variable environmental conditions should be performed before universal application of such models.

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.123-135
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• 2020

This study examined how the quality of Netflix service affects user satisfaction and continuance intention. This study classified the quality of Netflix service as content diversity, rate system appropriateness, recommendation system, N-screen service, binge viewing, and service quality, and examined the effect of each dimension on user satisfaction and continuous intention. We conduced an online survey on 202 Netflix users and analyzed the data with the SEM. Results are as follows. First, content diversity, recommendation system, binge-viewing and service quality are positively associated with user satisfaction. Second, the N-Screen service has neither direct nor indirect effects on continuance intention. However, rate system has a direct effect on continuance intention. On the other hand, content diversity, recommendation systems, binge-viewing, and quality of service affect continuance intention positively through user satisfaction. Finally, it is shown that user satisfaction and continuance intention have a significant static correlation as predicted.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5S
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• pp.1599-1608
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• 2000

Automated collaborative filtering is on the verge of becoming a popular technique to reduce overloaded information as well as to solve the problems that content-based information filtering systems cannot handle. In this paper, we describe three different algorithms that perform collaborative filtering: GroupLens that is th traditional technique; Best N, the modified one; and an algorithm that uses clustering. Based on the exeprimental results using real data, the algorithm using clustering is compared with the existing representative collaborative filtering agent algorithms such as GroupLens and Best N. The experimental results indicate that the algorithms using clustering is similar to Best N and better than GroupLens for prediction accuracy. The results also demonstrate that the algorithm using clustering produces the best performance according to the standard deviation of error rate. This means that the algorithm using clustering gives the most stable and the best uniform recommendation. In addition, the algorithm using clustering reduces the time of recommendation.

• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.586-594
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• 2004

Because web documents become creation and disappearance rapidly, users require the recommend system that offers users to browse the web document conveniently and correctly. One largely untapped source of knowledge about large data collections is contained in the cumulative experiences of individuals finding useful information in the collection. Recommendation systems attempt to extract such useful information by capturing and mining one or more measures of the usefulness of the data. The existing Information Filtering system has the shortcoming that it must have user's profile. And Collaborative Filtering system has the shortcoming that users have to rate each web document first and in high-quantity, low-quality environments, users may cover only a tiny percentage of documents available. And dynamic recommendation system using the user browsing pattern also provides users with unrelated web documents. This paper classifies these web documents using the similarity between the web documents under the web document type and extracts the user browsing sequential pattern DB using the users' session information based on the web server log file. When user approaches the web document, the proposed Dynamic recommendation system recommends Top N-associated web documents set that has high similarity between current web document and other web documents and recommends set that has sequential specificity using the extracted informations and users' session information.

• Journal of the Korea Organic Resources Recycling Association
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• v.29 no.2
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• pp.15-23
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• 2021

This study was conducted to evaluate the field application of the developed recommendation system in organic rice (Oriza sativa L.) paddy and to investigate the mineral nitrogen content in soil and rice productivity. According to the developed system, hairy vetch (HV), rye+rapeseed oil cake (R+OC), rapeseed oil cake (OC) for only basal fertilization (OC-B), OC for split application (OC-S), pig manure compost (PMC), and chemical fertilizer (CHM) were applied to paddy soil at the rate of 107~133 kg N/ha. Results were followed, unhulled rice yield of OC-S (111%), OC-B (110), R+OC (106), HV (101), and PMC (96) were no significantly different with CHM (100). Also there was positive correlation (R²=0.803^*) between unhulled rice yield and cumulative inorganic N in soil. For nitrogen use efficiency of rice, OC-B, OC-S, and R+OC were not significantly different with CHM. In conclusions, the developed organic materials use recommendation system was effective for organic rice productivity. It could be useful for organic farmer to apply the organic materials use recommendation system for rice.

• Journal of Crop Science and Biotechnology
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• v.11 no.2
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• pp.141-150
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• 2008

The response of grain yield(GY) and milled-rice protein content(PC) to crop growth status and nitrogen(N) rates at panicle initiation stage(PIS) is critical information for prescribing topdress N rate at PIS(Npi) for target GY and PC. Three split-split-plot experiments including various N treatments and rice cultivars were conducted in Experimental Farm, Seoul National University, Korea in 2003-2005. Shoot N density(SND, g N in shoot $m^{-2}$) and canopy reflectance were measured before N application at PIS, and GY, PC, and SND were measured at harvest. Data from the first two years(2003-2004) were used for calibrating the predictive models for GY, PC, and SND accumulated from PIS to harvest using SND at PIS and Npi by multiple stepwise regression. After that the calibrated models were used for calculating N requirement at PIS for each of nine plots based on the target PC of 6.8% and the values of SND at PIS that was estimated by canopy reflectance method in the 2005 experiment. The result showed that SND at PIS in combination with Npi were successful to predict GY, PC, and SND from PIS to harvest in the calibration dataset with the coefficients of determination ($R^2$) of 0.87, 0.73, and 0.82 and the relative errors in prediction(REP, %) of 5.5, 4.3, and 21.1%, respectively. In general, the calibrated model equations showed a little lower performance in calculating GY, PC, and SND in the validation dataset(data from 2005) but REP ranging from 3.3% for PC and 13.9% for SND accumulated from PIS to harvest was acceptable. Nitrogen rate prescription treatment(PRT) for the target PC of 6.8% reduced the coefficient of variation in PC from 4.6% in the fixed rate treatment(FRT, 3.6g N $m^{-2}$) to 2.4% in PRT and the average PC of PRT was 6.78%, being very close to the target PC of 6.8%. In addition, PRT increased GY by 42.1 $gm^{-2}$ while Npi increased by 0.63 $gm^{-2}$ compared to the FRT, resulting in high agronomic N-use efficiency of 68.8 kg grain from additional kg N. The high agronomic N-use efficiency might have resulted from the higher response of grain yield to the applied N in the prescribed N rate treatment because N rate was prescribed based on the crop growth and N status of each plot.