• 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.

• Journal of Korea Entertainment Industry Association
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• v.13 no.7
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• pp.1-13
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• 2019

This study tried to explore the contents of film critics' reviews reported in the press. Based on fifty nine Korean movies with over 100,000 audience in 2017, this study collected 1113 reviews from fifty five movies with the exception of four without reviews. This study focused on the correlation between film's overall quality and four evaluation items such as directing, acting, story, and the visual. Examining the difference in the report timing of the review, the length of the review, and the intensity of the opinion, this study also analyzed the relationship between the internal aspects of reviews and box office performance. According to the results, the valence of critics' reviews was generally positive. Looking at the difference of reporting time, this valence was higher in the week before release than in the release week of film. The evaluation items of reviews were highly covered both before movie release and in the opening week. These were significantly declined in the second week of release. In the relationship between the number of reviews by each movie and box office performance, a positive correlation was found.

• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.89-96
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• 2020

Linked Data allows structured data to be published in a standard way that datasets from various domains can be interlinked. With the rapid evolution of Linked Open Data(LOD), researchers are exploiting it to solve particular problems such as semantic similarity assessment. In this paper, we propose a method, on top of the basic concept of Linked Data Semantic Distance (LDSD), for calculating the Linked Data semantic distance between resources that can be used in the LOD-based recommender system. The semantic distance measurement model proposed in this paper is based on a similarity measurement that combines the LOD-based semantic distance and a new link weight using TF-IDF, which is well known in the field of information retrieval. In order to verify the effectiveness of this paper's approach, performance was evaluated in the context of an LOD-based recommendation system using mixed data of DBpedia and MovieLens. Experimental results show that the proposed method shows higher accuracy compared to other similar methods. In addition, it contributed to the improvement of the accuracy of the recommender system by expanding the range of semantic distance calculation.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.3
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• pp.121-128
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• 2015

A given product's online product reviews build up to form largely positive or negative reviews or mixed reviews that include both the positive and negative reviews. While the homogeneously positive or negative reviews help readers identify the generally praised or criticized product, the mixed reviews with minority opinions potentially contain valuable information about the product. We present a method of retrieving minority opinions from the online product reviews using the skewness of positive/negative reviews. The proposed method first classifies the positive/negative product reviews using a sentiment dictionary and then calculates the skewness of the classified results to identify minority reviews. Minority review retrieval experiments were conducted on smartphone and movie reviews, and the F1-measures were 24.6% (smartphone) and 15.9% (movie) and the accuracies were 56.8% and 46.8% when the individual reviews' sentiment classification accuracies were 85.3% and 78.8%. The theoretical performance of minority review retrieval is also discussed.

• The Journal of the Korea Contents Association
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• v.7 no.10
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• pp.19-26
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• 2007

Since collaborative filtering has used the nearest-neighborhood method based on item preference it cannot only reflect exact contents but also has the problem of sparsity and scalability. The item-based collaborative filtering has been practically used improve these problems. However it still does not reflect attributes of the item. In this paper, we propose the method of associative group using the FP-Tree to solve the problem of existing recommendation system. The proposed makes frequent item and creates association rule by using FP-Tree without occurrence of candidate set. We made the efficient item group using $\alpha-cut$ according to the confidence of the association rule. To estimate the performance, the suggested method is compared with Gibbs Sampling, Expectation Maximization, and K-means in the MovieLens dataset.

• The KIPS Transactions:PartB
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• v.14B no.7
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• pp.549-558
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• 2007

Collaborative filtering systems have problems when users rate items and the rated results depend on their feelings, as there is a possibility that the results include noise. The method proposed in this paper optimizes the matrix by excluding irrelevant ratings as information for recommendations from a user-item matrix using dispersion. It reduces the noise that results from predicting preferences based on original user ratings by inflecting the information for items and users on the matrix. The method excludes the ratings values of the utmost limits using a percentile to supply the defects of coefficient of variance and composes a weighted user-item matrix by combining the user coefficient of variance with the median of ratings for items. Finally, the preferences of the active user are predicted based on the weighted matrix. A large database of user ratings for movies from the MovieLens recommender system is used, and the performance is evaluated. The proposed method is shown to outperform earlier methods significantly.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2602-2608
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• 2013

User-based and item-based method have been developed as the solutions of the movie recommendation problem. However, these methods are faced with the sparsity problem and the problem of not reflecting user's rating respectively. In order to solve these problems, there is a research on the combination of the two methods using the concept of similarity. In reality, it is not free from the problem of sparsity, since it has a lot of parameters to be calculated. In this study, we propose a recommendation method using rating difference between items in order to complement this problem. This method is relatively free from the problem of sparsity, since it has less parameters to be calculated. And it can get more accurate results by reflecting the users rating to calculate the parameters. In experiments for the proposed method, the initial error is large, but the performance has been quickly stabilized after. In addition, it showed a 0.0538 lower average error compared to the existing method using similarity.

• The Journal of the Korea Contents Association
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• v.7 no.6
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• pp.1-8
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• 2007

In electronic commerce, it is important for users to recommend the proper item among large item sets with saving time and effort. Therefore, if the recommendation system can be recommended the suitable item, we will gain a good satisfaction to the user. In this paper, we proposed the associative relation clustering split method in the collaborative filtering in order to perform the accuracy and the scalability. We produce the lift between associative items using the ratings data. and then split the node group that consists of the item to improve an efficiency of the associative relation cluster. This method differs the association about the items of groups. If the association of groups is filled, the reminding items combine. To estimate the performance, the suggested method is compared with the K-means and EM in the MovieLens data set.

• Journal of the Korea Society of Computer and Information
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• v.17 no.9
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• pp.49-56
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• 2012

In this paper, we propose the method that decides the shot types using face detection technique. The shot types, such as close-up shot, medium shot, and long shot, can be applied as useful information for understanding narrative structure of movies. The narrative structure of movie is builded by characters. Also their mental and emotional changes become inextricably bound up with them of narrative. The shot types are decided by distance between character and camera. If put together above them, shot types can be found by using detection technique of face size of characters and understand narrative of movie. To do this, we propose the methodology to detect shot type by face detecting and implement the system to do it. Additionally, we evaluate the performance of the system. The implementation system has been evaluated as 95% for close-up shot detection and 90% for medium shot detection, while 53.3% is just detected for long shots.

• 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.