• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.265-283
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• 2022

MovieLens data consists of recorded movie evaluations that was often used to measure the evaluation score in the recommendation system research field. In this paper, we provide additional information obtained by clustering user-specific genre preference information through movie evaluation data and movie genre data. Because the number of movie ratings per user is very low compared to the total number of movies, the missing rate in this data is very high. For this reason, there are limitations in applying the existing clustering methods. In this paper, we propose a convex clustering-based method using the pairwise fused penalty motivated by the analysis of MovieLens data. In particular, the proposed clustering method execute missing imputation, and at the same time uses movie evaluation and genre weights for each movie to cluster genre preference information possessed by each individual. We compute the proposed optimization using alternating direction method of multipliers algorithm. It is shown that the proposed clustering method is less sensitive to noise and outliers than the existing method through simulation and MovieLens data application.

• 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 the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.526-529
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• 2003

Recommender systems or collaborative filtering are methods to identify potentially interesting or valuable items to a particular user Under the assumption that people with similar interest tend to like the similar types of items, these methods use a database on the preference of a set of users and predict the rating on the items that the user has not rated. Usually the preference of a particular user is liable to vary with time and this temporal variation may cause an inaccurate identification and prediction. In this paper we propose a method to adapt the temporal variation of the user preference in order to improve the predictive performance of a collaborative filtering algorithm. To be more specific, the correlation weight of the GroupLens system which is a general formulation of statistical collaborative filtering algorithm is modified to reflect only recent similarity between two user. The proposed method is evaluated for EachMovie dataset and shows much better prediction results compared with GrouPLens system.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.717-726
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• 2006

This study is to investigate the MAE of prediction value by collaborative filtering algorithm originated by GroupLens and improved algorithm. To decrease the MAE on the collaborative recommender system on user based, this research proposes the improved algorithm, which reduces the possibility of over estimation of active user's preference mean collaboratively using other user’s preference mean. The result shows the MAE of prediction by improved algorithm is better than original algorithm, so the active user's preference mean used in prediction formula is possibly over estimated.

• Information Systems Review
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• v.9 no.1
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• pp.85-103
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• 2007

The purpose of this study is to evaluate the performance of collaborative filtering recommender algorithms for better prediction accuracy of the customer's preference. The accuracy of customer's preference prediction is compared through the MAE of neighborhood based collaborative filtering algorithm and correspondence mean algorithm. It is analyzed by using MovieLens 1 Million dataset in order to experiment with the prediction accuracy of the algorithms. For similarity, weight used in both algorithms, commonly, Pearson's correlation coefficient and vector similarity which are used generally were utilized, and as a result of analysis, we show that the accuracy of the customer's preference prediction of correspondence mean algorithm is superior. Pearson's correlation coefficient and vector similarity used in two algorithms are calculated using the preference rating of two customers' co-rated movies, and it shows that similarity weight is overestimated, where the number of co-rated movies is small. Therefore, it is intended to increase the accuracy of customer's preference prediction through expanding the number of the existing co-rated movies.

• Journal of Korean Ophthalmic Optics Society
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• v.14 no.1
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• pp.31-38
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• 2009

Purpose: This study was to evaluate corneal topography, contrast sensitivity and ocular response of a RGP, back surface aspherical contact lens compared with a spherical contact lens. Methods: A total 37 subjects were fitted with a spherical lens in right eye and an aspherical in the left eye and were evaluated for changes in corneal topography and contrast sensitivity over a 2-month period. Results: Thirty-four of 37 subjects completed the 2-month study. The corneal topography did not show differences between spherical and aspherical RGP lenses. The eyes fitted with the aspherical lenses demonstrated a greater reduction in contrast sensitivity compared with their spherical counterparts under photopic condition. Subjects preferred comfort and ocular responses provided by the spherical lens. Conclusions: Corneal topography when comparing spherical and back surface aspherical RGP lenses did not show any significant difference in the subjects. Spherical RGP lens yields better contrast sensitivity and preference than aspherical RGP lens at photopic condition. Further investigation of aberrations induced by contact lens design is warranted to explain the observed differences in visual performance.

• 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.

• Journal of Korean Ophthalmic Optics Society
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• v.13 no.2
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• pp.9-16
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• 2008

To assess the preference and efficacy of empirical fitting methods with spheric and aspheric RGP lenses. Methods: Healthy 37 subjects were fitted with spheric design (diameter 9.3 mm) on right eye and aspheric design (dia 9.6 mm) on the left eye. Base curves which were fitted empirically (using on-K, Kavg-0.50D (or 1.00D) and manufacturer's recommended fitting guide) were compared with another base curve which obtained the best diagnostic fit with spheric and aspheric RGP lenses. The preference and fitting type (lid attachment or interpalpebral) for two design lenses were investigated 2 weeks after fitting RGP lenses. Results: Of 33 successful RGP lens-wearing subjects, 76% preferred spheric design compared with 24% of aspheric RGP lens wearers. Sixty seven percent were fitted with lid-attachment in spheric lenses, whereas 64% were fitted with lid-attachment in aspheric lenses. The acceptable fit success rates within ${\pm}$0.50D of base curves were 97% for the on-K fit, 100% for the Kavg-0.50D fit and 100% of the manufacturer's guide fit compared with the diagnostic fit in spheric design, whereas 91%, 79% and 94% reported on-K, Kavg-1.00D and manufacturer's guide, respectively, in aspheric design. Conclusions: Although aspheric RGP lenses are more popular in the Korean market, it is still preferable to fit subjects with spheric RGP lenses. Empirical fitting may be best accomplished with the spheric lenses using Kavg-0.50D fit and the manufacturer's fitting guide, whereas aspheric RGP lens designs are unacceptable lens fit based on empirical fitting.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.55-62
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• 2009

Collaborative filtering is a popular technique that recommends items based on the opinions of other people in recommender systems. Memory-based collaborative filtering which uses user database can be divided in user-based approaches and item-based approaches. User-based collaborative filtering predicts a user's preference of an item using the preferences of similar neighborhood, while item-based collaborative filtering predicts the preference of an item based on the similarity of items. This paper proposes a combined forecast scheme that predicts the preference of a user to an item by combining user-based prediction and item-based prediction using the ratio of the number of similar users and the number of similar items. Experimental results using MovieLens data set and the BookCrossing data set show that the proposed scheme improves the accuracy of prediction for movies and books compared with the user-based scheme and item-based scheme.

• Journal of Korean Ophthalmic Optics Society
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• v.14 no.1
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• pp.103-108
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• 2009

Purpose: The objective of this study is to compare and analyze the wearing status of refractive error correction devices of elders who reside in a city or in a small town district. Methods: Each of opticians from a small town or a city was selected for the study of wearing status of presbyopia correction device for each residential district in units of percentage. with the analysis of the age and gender distributions of the elders, numbers of elderly members, and the kinds of presbyopia correction. Results: The wearing rate of progressive lens was reduced in reverse proportion to the increase of the age for the people of presbyopia in a twon. Pepople in 60s living in a town perferred to wearing bifocal lens, but people of 50~60s preferred to single vision lenses. However, none of people living in a city who is diagnosed as presbyopia had refractive error correction device, and no one used bifical lenses. The progressive lens was mostly used in the people of 40~50s and using rate of those lenses reduced with the age; and single vision lens had the highest rate of in the 40~50s but no one wore it in the 70s. Conclusions: Among the refractive error correction devices, the progressive lens was most widely worn by presbyopia group who is living in a town or a city. In particular, the refractive error correction devices were most preferred in 40~50s of early presbyopia. The highest preference for the progressive lens in the people with the early presbyopia indicates that the wearing rate of the progressive will be increased in future. Therefore, the opportunity of systematic education on the progressive lens should be increased.