• The KIPS Transactions:PartB
• /
• v.13B no.6 s.109
• /
• pp.625-632
• /
• 2006

Collaborative filtering is a technology that aims at teaming predictive models of user preferences. Collaborative filtering systems have succeeded in Ecommerce market but they have shortcomings of high dimensionality and sparsity. In this paper we propose the nearest neighbor collaborative filtering method using non-negative matrix factorization(NNMF). We replace the missing values in the user-item matrix by using the user variance coefficient method as preprocessing for matrix decomposition and apply non-negative factorization to the matrix. The positive decomposition method using the non-negative decomposition represents users as semantic vectors and classifies the users into groups based on semantic relations. We compute the similarity between users by using vector similarity and selects the nearest neighbors based on the similarity. We predict the missing values of items that didn't rate by a new user based on the values that the nearest neighbors rated items.

• Journal of the Korean Data and Information Science Society
• /
• v.20 no.5
• /
• pp.809-818
• /
• 2009

Collaborative filtering approach predicts the preference of active user about specific items transacted on the e-commerce by using others' preference information. To improve the prediction accuracy through collaborative filtering approach, it must be needed to gain enough preference information of users' for predicting preference. But, a bit much information of users' preference might wrongly affect on prediction accuracy, and also too small information of users' preference might make bad effect on the prediction accuracy. This research suggests the method, which decides suitable numbers of neighbor users for applying collaborative filtering algorithm, improved by existing k nearest neighbors selection methods. The result of this research provides useful methods for improving the prediction accuracy and also refines exploratory data analysis approach for deciding appropriate numbers of nearest neighbors.

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.2
• /
• pp.135-146
• /
• 2010

In this paper, we propose an Recommendation System for supporting self-directed learning on e-learning marketplace. The key idea of this system is recommendation system using revised collaborative filtering to support marketplace. Exisiting collaborative filtering method consists of 3 stages as preparing low data, building familiar customer group by selecting nearest neighbor, creating recommendation list. This study designs recommendation system to support self-directed learning by using collaborative filtering added nearest neighbor learning course that considered industry and learning level. This service helps to select right learning course to learner in industry. Recommendation System can be built by many method and to recommend the service content including explicit properties using revised collaborative filtering method can solve limitations in existing content recommendation.

• Journal of Korea Multimedia Society
• /
• v.14 no.11
• /
• pp.1467-1477
• /
• 2011

This paper proposes a HPPS(Hybrid Preference Prediction System) design using the analysis of user profile and of the similarity among users precisely to predict the preference for custom-tailored service. Contrary to the existing NBCFA(Neighborhood Based Collaborative Filtering Algorithm), this paper is designed using these following rules. First, if there is no neighbor's commodity rating value in a preference prediction formula, this formula uses the rating average value for a commodity. Second, this formula reflects the weighting value through the analysis of a user's characteristics. Finally, when the nearest neighbor is selected, we consider the similarity, the commodity rating, and the rating frequency. Therefore, the first and second preference prediction formula made HPPS improve the precision by 97.24%, and the nearest neighbor selection method made HPPS improve the precision by 75%, compared with the existing NBCFA.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.5
• /
• pp.1708-1727
• /
• 2021

In the advent of the twenty-first century, human beings began to closely interact with technology. Today, technology is developing, and as a result, the world wide web (www) has a very important place on the Internet and the significant task is fulfilled by Web services. A lot of Web services are available on the Internet and, therefore, it is difficult to find matching Web services among the available Web services. The recommendation systems can help in fixing this problem. In this paper, our observation was based on the recommended method such as the collaborative filtering (CF) technique which faces some failure from the data sparsity and the cold-start problems. To overcome these problems, we first applied an ontology-based clustering and then the k-nearest neighbor (KNN) algorithm for each separate cluster group that effectively increased the data density using the past user interests. Then, user ratings were predicted based on the model-based approach, such as singular value decomposition (SVD) and the predictions used for the recommendation. The evaluation results showed that our proposed approach has a less prediction error rate with high accuracy after analyzing the existing recommendation methods.

• Journal of The Korean Association of Information Education
• /
• v.11 no.3
• /
• pp.281-288
• /
• 2007

Collaborative filtering is a method to predict preference items of a user based on the evaluations of items provided by others with similar preferences. Collaborative filtering helps general people make smart decisions in today's information society where information can be easily accumulated and analyzed. We designed, implemented, and evaluated a course recommendation system experimentally. This system can help university students choose courses they prefer to. Firstly, the system needs to collect the course preferences from students and store in a database. Users showing similar preference patterns are considered into similar groups. We use Pearson correlation as a similarity measure. We select K-nearest students to predict the unknown preferences of the student and provide a ranked list of courses based on the course preferences of K-nearest students. We evaluated the accuracy of the recommendation by computing the mean absolute errors of predictions using a survey on the course preferences of students.

• Proceedings of the Korean Information Science Society Conference
• /
• 2008.06c
• /
• pp.376-380
• /
• 2008

사회의 복잡화와 인터넷의 성장으로 폭발적으로 늘어나고 있는 정보들을 사용자가 모두 검토한 후 여과하기는 어려운 일이다. 이러한 문제를 보완하기 위해서 자동화된 정보 여과 기술이 사용되는데, k-최근접 이웃(k-nearest neighbor) 알고리즘은 그 구현이 간단하며 비교적 정확하여 가장 널리 쓰이고 있는 알고리즘 중 하나이다. k 개의 최근접 이웃들로부터 평가값을 계산하는 데 흔히 쓰이는 방법은 상관계수를 이용한 가중치에 기반하는 것이다. 본 논문에서는 이를 보완하여 대규모 데이터에 대해서도 속도는 크게 저하되지 않으며 정확도는 대폭 향상시킬 수 있는 방법을 적용하였다. 또한, 최근접 이웃을 구하는 거리함수로 다양한 방법을 시도하였다. 영화추천을 위한 실제 데이터에 대한 실험 결과, 속도의 저하는 미미하였으나 정확도에 있어서는 크게 향상된 결과를 가져올 수 있었다.

• Journal of Information Processing Systems
• /
• v.15 no.5
• /
• pp.1141-1155
• /
• 2019

Today, most approaches used in the recommendation system provide correct data prediction similar to the data that users need. The method that researchers are paying attention and apply as a model in the recommendation system is the communities' detection in the big social network. The outputted result of this approach is effective in improving the exactness. Therefore, in this paper, the personalized movie recommendation system that combines data mining for the k-clique method is proposed as the best exactness data to the users. The proposed approach was compared with the existing approaches like k-clique, collaborative filtering, and collaborative filtering using k-nearest neighbor. The outputted result guarantees that the proposed method gives significant exactness data compared to the existing approach. In the experiment, the MovieLens data were used as practice and test data.

• Journal of KIISE
• /
• v.42 no.11
• /
• pp.1380-1385
• /
• 2015

Collaborative filtering is one of the most successfully used methods for recommender systems and has been utilized in various areas such as books and music. The key point of this method is selecting the most proper recommenders, for which various similarity measures have been studied. To improve recommendation performance, this study analyzes problems of existing recommender selection methods based on similarity and presents a method of dynamically determining recommenders based on the rate of co-rated items as well as similarity. Examination of performance with varying thresholds through experiments revealed that the proposed method yielded greatly improved results in both prediction and recommendation qualities, and that in particular, this method showed performance improvements with only a few recommenders satisfying the given thresholds.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.11
• /
• pp.149-156
• /
• 2021

Among various implementation techniques of recommender systems, collaborative filtering selects nearest neighbors with high similarity based on past rating history, recommends products preferred by them, and has been successfully utilized by many commercial sites. Accurate estimation of similarity is an important factor that determines performance of the system. Various similarity measures have been developed, which are mostly based on integrating traditional similarity measures and several indices already developed. This study suggests a similarity measure of a novel approach. It separates the common rating area between two users by the magnitude of ratings, estimates similarity for each subarea, and integrates them with weights. This enables identifying similar subareas and reflecting it onto a final similarity value. Performance evaluation using two open datasets is conducted, resulting in that the proposed outperforms the previous one in terms of prediction accuracy, rank accuracy, and mean average precision especially with the dense dataset. The proposed similarity measure is expected to be utilized in various commercial systems for recommending products more suited to user preference.