• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.101-107
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• 2014

This paper presents an efficient distributed recommendation system using clustering collaborative filtering algorithm in distributed computing environments. The system was built based on Hadoop distributed computing platform, where distributed Min-hash clustering algorithm is combined with user based collaborative filtering algorithm to optimize recommendation performance. Experiments using Movie Lens benchmark data show that the proposed system can reduce the execution time for recommendation compare to sequential system.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.185-198
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• 2021

With the development of Internet technology, because traditional recommendation algorithms cannot learn the in-depth characteristics of users or items, this paper proposed a recommendation algorithm based on the AMITI(attention mechanism and improved TF-IDF) to solve this problem. By introducing the two-layer attention mechanism into the CNN, the feature extraction ability of the CNN is improved, and different preference weights are assigned to item features, recommendations that are more in line with user preferences are achieved. When recommending items to target users, the scoring data and item type data are combined with TF-IDF to complete the grouping of the recommendation results. In this paper, the experimental results on the MovieLens-1M data set show that the AMITI algorithm improves the accuracy of recommendation to a certain extent and enhances the orderliness and selectivity of presentation methods.

• The Journal of the Korea Contents Association
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• v.8 no.3
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• pp.1-7
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• 2008

Due to the development of ubiquitous computing, a wide variety of information is being produced and distributed rapidly in digital form. In this excess of information, it is not easy for users to search and find their desired information in short time. In this paper, we propose the personalized item recommendation using the image based filtering. This research uses the image based filtering which is extracting the feature from the image data that a user is interested in, in order to improve the superficial problem of content analysis. We evaluate the performance of the proposed method and it is compared with the performance of previous studies of the content based filtering and the collaborative filtering in the MovieLens dataset. And the results have shown that the proposed method significantly outperforms the previous methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.267-269
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• 2019

There are many recommendation systems offer an effort to get better preciseness the information to the users. In order to further improve more accuracy, the social network analysis method which is used to analyze data to community detection in social networks was introduced in the recommendation system and the result shows this method is improving more accuracy. In this paper, we propose a movie recommendation system using social network analysis and normalized discounted cumulative gain with the best accuracy. To estimate the performance, the collaborative filtering using the k nearest neighbor method, the social network analysis with collaborative filtering method and the proposed method are used to evaluate the MovieLens data. The performance outputs show that the proposed method get better the accuracy of the movie recommendation system than any other methods used in this experiment.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.34-36
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• 2022

Over the last decades, information has increased exponentially due to SNS(Social Network Service), IoT devices, World Wide Web, and many others. Therefore, it was monumentally hard to offer a good service or set of recommendations to consumers. To surmount this obstacle numerous research has been conducted in the Data Mining field. Different and new recommendation models have emerged. In this paper, we proposed a Content-based movie recommendation system using demographic information of users and the average rating for genres. We used MovieLens Dataset to proceed with our experiment.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.319-321
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• 2002

협력적 추천 알고리즘의 성능향상을 위한 많은 연구들이 진행되고 연구 결과로 다양한 협력적 추천 기법들이 제안되고 있다. 이러한 연구에서는 EachMovie, MovieLens등의 선호도(Rating) 값을 기반으로 하는 데이터를 대상으로 추천의 효율을 높이고자 하고 있다. 그러나 실세계에서 우리가 얻을 수 있는 원 거래 데이터(Raw Transaction Data)는 선호도 값을 갖고 있지 않다. 따라서 실세계의 구매 데이터에 효과적인 추천을 하기 위해서는 기존의 선호도 기반 알고리즘이 아닌 구매 정보만을 기반으로 하는 변경된 협력적 추천 알고리즘이 필요하다. 본 논문에서는 연관규칙 탐사 기법에서 사용하는 확신도(confidence)를 유사도식에 사용하고 이를 기반으로 선호도를 예측하는 구매 기반의 협력적 추천 알고리즘을 제안한다.

• The Journal of the Korea Contents Association
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• v.8 no.7
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• pp.17-25
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• 2008

The smart home environment focuses on recognizing the context and physical entities. And this is mainly focused on the personalized service supplied conversational interactions. In this paper, we proposed the recommendation using the context awareness based information filtering that dynamically applied by the context awareness as well as the meta data in the smart home. The proposed method defined the context information and recommended the profited service for the user’s taste using the context awareness based information filtering. Accordingly, the satisfaction of users and the quality of services will be improved the efficient recommendation by supporting the distributed processing as well as the mobility of services. Finally, to evaluate the performance of the proposed method, this study applies to MovieLens dataset in the OSGi framework, and it is compared with the performance of previous studies.

• The Journal of the Korea Contents Association
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• v.8 no.2
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• pp.1-8
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• 2008

The information system in which users can utilize to control and to get the filtered information efficiently has appeared. Content-based filtering can reflect content information, and it provides recommendation by comparing the feature information about item and the profile of preference. This has the shortcoming of the varying accuracy of prediction depending on teaming method. This paper suggests the discovery of preference through learning the profile for the content-based filtering. This study improves the accuracy of recommendation through learning the profile according to granting the preference of 6 levels to estimated value in order to solve the problem. Finally, to evaluate the performance of the proposed method, this study applies to MovieLens dataset, and it is compared with the performance of previous studies.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.59-67
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• 2013

In this paper, we propose the LAR_CF, latent attribute rating-based collaborative filtering, that is robust to data sparsity problem which is one of traditional problems caused of decreasing rating prediction accuracy. As compared with that existing collaborative filtering method uses a preference rating rated by users as feature vector to calculate similarity between objects, the proposed method improves data sparsity problem using unique attributes of two target objects with existing explicit preference. We consider MovieLens 100k dataset and its item attributes to evaluate the LAR_CF. As a result of artificial data sparsity and full-rating experiments, we confirmed that rating prediction accuracy can be improved rating prediction accuracy in data sparsity condition by the LAR_CF.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.218-225
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• 2007

Recommendation system is one way of implementing personalized service. The collaborative filtering is one of the major techniques that have been employed for recommendation systems. It has proven its effectiveness in the recommendation systems for such domain as motion picture or music. However, it has some limitations, i.e., sparsity and scalability. In this research, as one way of overcoming such limitations, we proposed the stepwise collaborative filtering method. To show the practicality of our proposed method, we designed and implemented a movie recommendation system which we shall call Step_CF, and its performance was evaluated using MovieLens data. The performance of Step_CF was better than that of Basic_CF that was implemented using the original collaborative filtering method.