• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.6
• /
• pp.237-244
• /
• 2022

In this paper, we present VAE-based recommendation using online behavior log and purchase history to overcome data sparsity and cold start. To generate a variable for customers' purchase history, embedding and dimensionality reduction are applied to the customers' purchase history. Also, Variational Autoencoders are applied to online behavior and purchase history. A total number of 12 variables are used, and nDCG is chosen for performance evaluation. Our experimental results showed that the proposed VAE-based recommendation outperforms SVD-based recommendation. Also, the generated purchase history variable improves the recommendation performance.

• Korean Journal of Soil Science and Fertilizer
• /
• v.34 no.1
• /
• pp.64-70
• /
• 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 Information Technology Applications and Management
• /
• v.29 no.3
• /
• pp.43-55
• /
• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.

• KIISE Transactions on Computing Practices
• /
• v.21 no.2
• /
• pp.121-125
• /
• 2015

In recent years, research on collaborative filtering-based recommendation systems emphasized the accuracy of rating predictions, and this has led to an increase in computation time. As a result, such systems have divergeded from the original purpose of making quick recommendations. In this paper, we propose a recommendation algorithm that uses a Preference Difference Metric to reduce the computation time and to maintain adequate performance. The system recommends items according to their preference classification.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.48 no.2
• /
• pp.78-85
• /
• 2011

In this paper, a research on multi-agent is carried out in order to develop a system that can provide drivers with real-time route recommendation by reflecting Dynamic Environment Information which acts as an agent in charge of Driver's trait, road condition and Route recommendation system. DEI is equivalent to number of n multi-agent and is an environment variable which is used in route recommendation system with optimal routes for drivers. Route recommendation system which reflects DEI can be considered as a new field of topic in multi-agent research. The representative research of Multi-agent, the Prey Pursuit Problem, was used to generate a fresh solution. In this thesis paper, you will be able to find the effort of indulging the lack of Prey Pursuit Problem,, which ignored practicality. Compared to the experiment, it was provided a real practical experiment applying the algorithm, the new Ant-Q method, plus a comparison between the strategies of the established direction vector was put into effect. Together with these methods, the increase of the efficiency was able to be proved.

• Journal of Information Technology and Architecture
• /
• v.9 no.1
• /
• pp.57-66
• /
• 2012

Recently, with a rapidly growing of the mobile content market, a variety of mobile-based applications are being launched. But mobile devices, compared to the average computer, take a lot of effort and time to get the final contents you want to use due to the restrictions such as screen size and input methods. To solve this inconvenience, a recommender system is required, which provides customized information that users prefer by filtering and forecasting the information.In this study, an tailored multi-information recommendation system utilizing a Personalized information recommendation system on smartphone is proposed. Filtering of information is to predict and recommend the information the individual would prefer to by using the user-based collaborative filtering. At this time, the degree of similarity used for the user-based collaborative filtering process is Euclidean distance method using the Pearson's correlation coefficient as weight value.As a real applying case to evaluate the performance of the recommender system, the scenarios showing the usefulness of recommendation service for the actual restaurant is shown. Through the comparison experiment the augmented reality based multi-recommendation services to the existing single recommendation service, the usefulness of the recommendation services in this study is verified.

• Journal of Biosystems Engineering
• /
• v.42 no.2
• /
• pp.98-103
• /
• 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.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.5S
• /
• pp.1599-1608
• /
• 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 Korea Society of Digital Industry and Information Management
• /
• v.8 no.4
• /
• pp.43-51
• /
• 2012

Web 3.0 is the next-generation of the World Wide Web and is included two main platforms, semantic technologies and social computing environment. The basic idea of web 3.0 is to define structure data and link them in order to more effective discovery, automation, integration, and reuse across various applications. The semantic technologies represent open standards that can be applied on the top of the web. The social computing environment allows human-machine co-operations and organizing a large number of the social web communities. In the recent years, recommender systems have been combined with ontologies to further improve the recommendation by adding semantics to the context on the web 3.0. In this paper, we study previous researches about recommendation method and propose a recommendation method based on web 3.0. Our method scores documents based on context tags and social network services. Our social scoring model is computed by both a tagging score of a document and a tagging score of a document that was tagged by a user's friends.

• Journal of Fashion Business
• /
• v.25 no.5
• /
• pp.25-37
• /
• 2021

As the online shopping market grows, channels in the mobile shopping environment have become increasingly diverse as a wide variety of products are introduced every day. This study investigated the effects of the self-construal type, mobile product recommendation system type, and fashion product type on purchase intention. The experimental design of this study was a 2 (self-construal type: independent vs. interdependent) × 2 (product recommendation system: bestseller vs. content-based) × 2 (fashion product type: utilitarian vs. hedonic) 3-way mixed ANOVA. Women (n = 387) in their 20 to 30s residing in Seoul and the Gyeonggi area participated in the study. The data were analyzed with the SPSS 24 program and 3-way ANOVA and simple main effects analyses were conducted. The results were as follows. First, self-construal, product recommendation, and fashion product types had a statistically significant impact on purchase intention. Second, fashion product and consumers' self-construal types had significant interaction effects on purchase intention. Finally, product recommendation and fashion product and self-construal types showed significant 3-way interaction effects on purchase intention. The study confirmed an interaction between the self-construal, type of product recommendation system, and the type of fashion product used in influencing purchase intention.