• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.299-304
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• 2006

최근 전자상거래 사이트에서는 각 고객에게 개별화된 서비스를 제공하기 위한 노력을 기울이고 있으며, 추천시스템은 이러한 개별화된 서비스를 제공하는데 중요한 역할을 하고 있다. 전자상거래 추천시스템에 대한 최근 연구 동향 중 하나는 고객의 탐색 및 행동 패턴 데이터를 이용하여 각 상품에 대한 선호도를 추정하고, 이를 바탕으로 한 추천시스템을 개발하는 것이다. 본 논문에서는 이와 같이 추정한 선호도 데이터에 차원 감소 기법을 적용한 추천시스템을 개발하였으며, 이를 기존의 협업적 필터링을 이용한 방법과 비교하였다. 실험용 전자상거래 사이트로부터 수집한 데이터를 바탕으로 두 방법을 비교하여, 추천 상품 수가 지나치게 크지 않을 때에는 차원 감소 기법을 이용한 방법의 성능이 협업적 필터링을 이용한 방법의 성능과 유사하거나 더 우수하다는 것을 보였다.

• Management Science and Financial Engineering
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• v.12 no.2
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• pp.71-85
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• 2006

Collaborative filtering, among other recommender systems, has been known as the most successful recommendation technique. However, it requires the user-item rating data, which may not be easily available. As an alternative, some collaborative filtering algorithms have been developed recently by utilizing the market basket data in the form of the binary user-item matrix. Viewing the recommendation scheme as a two-class classification problem, we proposed a new collaborative filtering scheme using a regularized discriminant analysis applied to the binary user-item data. The proposed discriminant model was built in terms of the major principal components and was used for predicting the probability of purchasing a particular item by an active user. The proposed scheme was illustrated with two modified real data sets and its performance was compared with the existing user-based approach in terms of the recommendation precision.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.648-649
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• 2016

일반적으로 협업 핑터랭 기반의 추천 시스템에서는 사용자와 아이템 간의 상호 작용이 희박하게 나타나는 문제 때문에 성능상의 한계점을 가지고 있다. 이 문제는 전통적으로 사용되었던 기계 학습의 입력 특성들이 의미적으로 관계가 없도록, 독립적으로 표현하기 때문이다. 본 논문에서는 임베딩 기법을 이용하여 서로 독립적으로 표현되었던 아이템들을 의미적으로 표현되는 벡터로 바꾸고, 최근 협업 필터링 기반의 추천 시스템으로 많이 사용되는 RNN을 사용하여 모델링한 시스템을 제안한다. 제안된 모델은 최근에 발표된 추천시스템들과 동등하거나 그 이상의 성능을 보일 것으로 기대된다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.11a
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• pp.602-609
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• 2007

Combining collaborative filtering with some other technique is most common in hybrid recommender systems. As many recommended items from collaborative filtering seem to be similar with respect to content, the collaborative-content hybrid system suffers in terms of quality recommendation and recommending new items as well. To alleviate such problem, we have developed a novel method that uses a diversity metric to select the dissimilar items among the recommended items from collaborative filtering, which together with the input when fed into content space let us improve and include new items in the recommendation. We present experimental results on movielens dataset that shows how our approach performs better than simple content-based system and naive hybrid system

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.9
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• pp.835-840
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• 2008

This paper proposes an enhanced recommendation technique for personalized e-commerce portal analyzing various attitudes of customer. The attitudes are classifies into three types such as "purchasing product", "adding product to shopping cart", and "viewing the product information". We implicitly track customer attitude to estimate the rating of products for recommending products. We classified user groups which have similar preference for each item using implicit user behavior. The preference similarity is estimated using the Cross Correlation Coefficient. Our recommendation technique shows a high degree of accuracy as we use age and gender to group the customers with similar preference. In the experimental section, we show that our method can provide better performance than other traditional recommender system in terms of accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.6
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• pp.1183-1190
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• 2017

With the development of network and IT technology, people are searching and purchasing items they want, not bounded by places. Therefore, there are various studies on how to solve the scalability problem due to the rapidly increasing data in the recommendation system. In this paper, we propose an item-based collaborative filtering method using Tag weight and a recommendation technique using MapReduce method, which is a distributed parallel processing method. In order to improve speed and efficiency, the proposed method classifies items into categories in the preprocessing and groups according to the number of nodes. In each distributed node, data is processed by going through Map-Reduce step 4 times. In order to recommend better items to users, item tag weight is used in the similarity calculation. The experiment result indicated that the proposed method has been more enhanced the appropriacy compared to item-based method, and run efficiently on the large amounts of data.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.107-121
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• 2005

Recently, many researches on recommendation systems and collaborative filtering have been proceeding in both research and practice. However, although product items may have multi-valued attributes, previous studies did not reflect the multi-valued attributes. To overcome this limitation, this paper proposes new methodology for recommendation system. The proposed methodology uses multi-valued attributes based on clustering technique for items and applies the collaborative filtering to provide accurate recommendations. In the proposed methodology, both user clustering-based CBR and item attribute clustering-based CBR technique have been applied to the collaborative filtering to consider correlation of item to item as well as correlation of user to user. By using multi-valued attribute-based clustering technique for items, characteristics of items are identified clearly. Extensive experiments have been performed with MovieLens data to validate the proposed methodology. The results of the experiment show that the proposed methodology outperforms the benchmarked methodologies: Case Based Reasoning Collaborative Filtering (CBR_CF) and User Clustering Case Based Reasoning Collaborative Filtering (UC_CBR_CF).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.4
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• pp.877-885
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• 2011

A collaborative filtering(CF) is the most widely used technique in recommender system. However, CF has sparsity and scalability problems. These problems reduce the accuracy of recommendation and extensive studies have been made to solve these problems, In this paper, we proposed a method that uses a weight so as to solve these problems. After creating a user-item matrix, the proposed method analyzes information about users who prefer the item only by using data with a rating over 4 for enhancing the accuracy in the recommendation. The proposed method uses information about the genre of the item as well as analyzed user information as a weight during the calculation of similarity, and it calculates prediction by using only data for which the similarity is over a threshold and uses the data as the rating value of unrated data. It is possible simultaneously to reduce sparsity and to improve accuracy by calculating prediction through an analysis of the characteristics of an item. Also, it is possible to conduct a quick classification based on the analyzed information once a new item and a user are registered. The experiment result indicated that the proposed method has been more enhanced the accuracy, compared to item based, genre based methods.

• The KIPS Transactions:PartD
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• v.9D no.6
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• pp.1009-1016
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• 2002

We design and implement a new case-based recommender system using implicit rating information for a digital content site. Our system consists of the User Profile Generation module, the Similarity Evaluation and Recommendation module, and the Personalized Mailing module. In the User Profile Generation Module, we define intra-attribute and inter-attribute weight deriver from own's past interests of a user stored in the access logs to extract individual preferences for a content. A new similarity function is presented in the Similarity Evaluation and Recommendation Module to estimate similarities between new items set and the user profile. The Personalized Mailing Module sends individual recommended mails that are transformed into platform-independent XML document format to users. To verify the efficiency of our system, we have performed experimental comparisons between the proposed model and the collaborative filtering technique by mean absolute error (MAE) and receiver operating characteristic (ROC) values. The results show that the proposed model is more efficient than the traditional collaborative filtering technique.

• Asia pacific journal of information systems
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• v.17 no.4
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• pp.187-206
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• 2007

The development of computer and information technology has been combined with the information superhighway internet infrastructure, so information widely spreads not only in special fields but also in the daily lives of people. Information ubiquity influences the traditional way of transaction, and leads a new E-commerce which distinguishes from the existing E-commerce. Not only goods as physical but also service as non-physical come into E-commerce. As the scale of E-Commerce is being enlarged as well. It keeps people from finding information they want. Recommender systems are now becoming the main tools for E-Commerce to mitigate the information overload. Recommender systems can be defined as systems for suggesting some Items(goods or service) considering customers' interests or tastes. They are being used by E-commerce web sites to suggest products to their customers who want to find something for them and to provide them with information to help them decide which to purchase. There are several approaches of recommending goods to customer in recommender system but in this study, the main subject is focused on collaborative filtering technique. This study presents a possibility of pre-evaluation for the prediction performance of customer's preference in collaborative filtering before the process of customer's preference prediction. Pre-evaluation for the prediction performance of each customer having low performance is classified by using the statistical features of ratings rated by each customer is conducted before the prediction process. In this study, MovieLens 100K dataset is used to analyze the accuracy of classification. The classification criteria are set by using the training sets divided 80% from the 100K dataset. In the process of classification, the customers are divided into two groups, classified group and non classified group. To compare the prediction performance of classified group and non classified group, the prediction process runs the 20% test set through the Neighborhood Based Collaborative Filtering Algorithm and Correspondence Mean Algorithm. The prediction errors from those prediction algorithm are allocated to each customer and compared with each user's error. Research hypothesis : Two research hypotheses are formulated in this study to test the accuracy of the classification criterion as follows. Hypothesis 1: The estimation accuracy of groups classified according to the standard deviation of each user's ratings has significant difference. To test the Hypothesis 1, the standard deviation is calculated for each user in training set which is divided 80% from MovieLens 100K dataset. Four groups are classified according to the quartile of the each user's standard deviations. It is compared to test the estimation errors of each group which results from test set are significantly different. Hypothesis 2: The estimation accuracy of groups that are classified according to the distribution of each user's ratings have significant differences. To test the Hypothesis 2, the distributions of each user's ratings are compared with the distribution of ratings of all customers in training set which is divided 80% from MovieLens 100K dataset. It assumes that the customers whose ratings' distribution are different from that of all customers would have low performance, so six types of different distributions are set to be compared. The test groups are classified into fit group or non-fit group according to the each type of different distribution assumed. The degrees in accordance with each type of distribution and each customer's distributions are tested by the test of ${\chi}^2$ goodness-of-fit and classified two groups for testing the difference of the mean of errors. Also, the degree of goodness-of-fit with the distribution of each user's ratings and the average distribution of the ratings in the training set are closely related to the prediction errors from those prediction algorithms. Through this study, the customers who have lower performance of prediction than the rest in the system are classified by those two criteria, which are set by statistical features of customers ratings in the training set, before the prediction process.