• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.125-131
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• 2017

It is not easy for the user to find the information that is appropriate for the user among the suddenly increasing information in recent years. One of the ways to help individuals make decisions in such a lot of information is the recommendation system. Although there are many recommendation methods for such recommendation systems, a representative method is collaborative filtering. In this paper, we design and implement the movie recommendation system on user-based collaborative filtering of apache mahout. In addition, Pearson correlation coefficient is used as a method of measuring the similarity between users. We evaluate Precision and Recall using the MovieLens 100k dataset for performance evaluation.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.4
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• pp.85-96
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• 2022

Personalized recommendation technology is one of the most important technologies in electronic commerce environment. It helps users overcome information overload by suggesting information that match user's interests. In e-commerce environment, both mobile device users and smart device users have risen dramatically. It creates new challenges. Our method suggests product information that match user's device interests beyond only user's interests. We propose a device-centered personalized recommendation method. Our method uses both purchase and share behavior for user's devices interests. Moreover, it considers data type preference for each device. This paper presents a new recommendation method and algorithm. Then, an e-commerce scenario with a computer, a smartphone and an AI-speaker are described. The scenario shows our work is better than previous researches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2903-2923
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• 2023

Knowledge recommendation is a type of recommendation system that recommends knowledge content to users in order to satisfy their needs. Although using graph neural networks to extract data features is an effective method for solving the recommendation problem, there is information loss when modeling real-world problems because an edge in a graph structure can only be associated with two nodes. Because one super-edge in the hypergraph structure can be connected with several nodes and the effectiveness of knowledge graph for knowledge expression, a dual-channel hypergraph convolutional neural network model (DCHC) based on hypergraph structure and knowledge graph is proposed. The model divides user data and knowledge data into user subhypergraph and knowledge subhypergraph, respectively, and extracts user data features by dual-channel hypergraph convolution and knowledge data features by combining with knowledge graph technology, and finally generates recommendation results based on the obtained user embedding and knowledge embedding. The performance of DCHC model is higher than the comparative model under AUC and F1 evaluation indicators, comparative experiments with the baseline also demonstrate the validity of DCHC model.

• The KIPS Transactions:PartD
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• v.15D no.4
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• pp.463-474
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• 2008

The advancement of information technology and the popularization of Internet has explosively increased the amount of multimedia contents. Therefore, the requirement of multimedia recommendation to satisfy a user's needs increases fastly. Up to now, CF is used to recommend general items and multimedia contents. However, general CF doesn't reflect visual characteristics of image contents so that it can't be adaptable to image recommendation. Besides, it has limitations in new item recommendation, the sparsity problem, and dynamic change of user preference. In this paper, we present new image recommendation method FBCF (Feature Based Collaborative Filtering) to resolve such problems. FBCF builds new user profile by clustering visual features in terms of user preference, and reflects user's current preference to recommendation by using preference feedback. Experimental result using real mobile images demonstrate that FBCF outperforms conventional CF by 400% in terms of recommendation ratio.

• Journal of the Korean Society for information Management
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• v.33 no.1
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• pp.317-336
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• 2016

Nowadays users spend more time and effort to find what they want because of information overload. To solve the problem, scientific article recommendation system analyse users' needs and recommend them proper articles. However, most of the scientific article recommendation systems neglected the core part, user profile. Therefore, in this paper, instead of mean which applied in user profile in previous studies, New TPIPF (Topic Proportion-Inverse Paper Frequency) was applied to scientific article recommendation system. Moreover, the accuracy of two scientific article recommendation systems with above different methods was compared with experiments of public dataset from online reference manager, CiteULike. As a result, the proposed scientific article recommendation system with TPIPF was proven to be better.

• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.33-43
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• 2017

The content curation service through big data analysis is receiving great attention in various content fields, such as film, game, music, and book. This service recommends personalized contents to the corresponding user based on user's preferences. The existing book curation systems recommended books to users by using bibliographic citation, user profile or user log data. However, these systems are difficult to recommend books related to character names or spatio-temporal information in text contents. Therefore, in this paper, we suggest a personalized book curation system based on integrated mining of a book. The proposed system consists of mining system, recommendation system, and visualization system. The mining system analyzes book text, user information or profile, and SNS data. The recommendation system recommends personalized books for users based on the analysed data in the mining system. This system can recommend related books using based on book keywords even if there is no user information like new customer. The visualization system visualizes book bibliographic information, mining data such as keyword, characters, character relations, and book recommendation results. In addition, this paper also includes the design and implementation of the proposed mining and recommendation module in the system. The proposed system is expected to broaden users' selection of books and encourage balanced consumption of book contents.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.1
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• pp.23-35
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• 2017

With the popularization of mobile devices, the number of social network service users is increasing, thereby the amount of data is also increasing accordingly. As Internet of Things environment is expanding to connect things and people, there is information much more than before. In such an environment, it becomes very important to recommend the necessary information to the user. In this paper, we propose a recommendation method that considers new users in IoT environment. In the proposed method, we recommend the information by applying the centrality-based social network analysis method to the recommendation method using the social relationships in the social IoT. We describe the seven-step recommendation method and apply them to the music circle scenario of the IoT environment. Through the music circle scenario, we show that we can recommend more suitable information to new users in the IoT environment than the existing recommendation method.

• Asia pacific journal of information systems
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• v.16 no.4
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• pp.217-238
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• 2006

In this study, we propose a virtual community recommendation model based on user behavioral models. It is designed to recommend optimal virtual communities for an active user by applying case-based reasoning (CBR) using behavioral factors suggested in the technology acceptance model (TAM) and its extensions. Also, it is designed to filter its case-base by considering the user's needs type before applying CBR. To test the usefulness of our model, we conduct two-step validation - experimental validation for the collected data, and survey validation for investigating the actual satisfaction level. Experimental results show that our model presents effective recommendation results in an efficient way. In addition, they also show that the information on the user's needs type may generate opportunities for cross-selling other commercial items.

• Journal of the Korea Society of Computer and Information
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• v.23 no.4
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• pp.89-95
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• 2018

It is not easy for the user to find the information that is appropriate for the user among the suddenly increasing information in recent years. One of the ways to help individuals make decisions in such a lot of information is the recommendation system. Although there are many recommendation methods for such recommendation systems, a representative method is collaborative filtering. In this paper, we design and implement the movie recommendation system on user-based collaborative filtering of apache mahout based on mongoDB. In addition, Pearson correlation coefficient is used as a method of measuring the similarity between users. We evaluate Precision and Recall using the MovieLens 100k dataset for performance evaluation.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.6-10
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• 2009

In these days, recommendation service in mobile environments is in the limelight due to the spread of mobile devices and an increase of information owing to advancement of computer network. The restaurant recommendation system reflecting user preference was proposed. This system uses Bayesian network to model user preference and analytical hierarchical process to recommend restaurants, but static inference model for user preference used in the system has some limitations that cannot manage changing user preference and enormous user survey must be preceded. This paper proposes a learning method for Bayesian network based on user requests. The proposed method is implemented on mobile devices and desktop, and we show the possibility of the proposed method through experiments.