• Journal of the Korean Institute of Plant Engineering
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• v.23 no.4
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• pp.57-64
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• 2018

User-based collaborative filtering is a method of recommending an item to a user based on the preference of the neighbor users who have similar purchasing history to the target user. User-based collaborative filtering is based on the fact that users are strongly influenced by the opinions of other users with similar interests. Item-based collaborative filtering is a method of recommending an item by comparing the similarity of the user's previously preferred items. In this study, we create a recommendation model using user-based collaborative filtering and item-based collaborative filtering with consumer's consumption data. Collaborative filtering is performed by using RFM (recency, frequency, and monetary) technique with purchasing data to recommend items with high purchase potential. We compared the performance of the recommendation system with the purchase amount and the performance when applying the RFM method. The performance of recommendation system using RFM technique is better.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.1062-1072
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• 2004

Collaborative filtering systems based on ｛user-document｝ matrix are effective in recommending web documents to user. But they have a shortcoming of decreasing the accuracy of recommendations by the first rater problem and the sparsity. This paper proposes the automatic preference rating method that generates user profile to solve the shortcoming. The profile in this paper is content-based collaborative user profile. The content-based collaborative user profile is generated by combining a content-based user profile with a collaborative user profile by mutual information method. Collaborative user profile is based on ｛user-document｝ matrix in collaborative filtering system, thus, content-based user profile is generated by relevance feedback in content-based filtering systems. After normalizing combined content-based collaborative user profiles, it automatically rates user preference by reflecting normalized profile in ｛user-document｝matrix of collaborative filtering systems. We evaluated our method on a large database of user ratings for web document and it was certified that was more efficient than existent methods.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.05a
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• pp.232-239
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• 2005

Collaborative-filtering-enabled Web sites that recommend books, CDs, movies, and so on, have become very popular on the Internet. Such sites recommend items to a user on the basis of the opinions of other users with similar tastes. This paper discuss an approach to collaborative filtering based on the Simple Bayesian and apply this model to two variants of the collaborative filtering. One is user-based collaborative filtering, which makes predictions based on the users' similarities. The other is item-based collaborative filtering which makes predictions based on the items' similarities. To evaluate the proposed algorithms, this paper used a database of movie recommendations. Empirical results show that the proposed Bayesian approaches outperform typical correlation-based collaborative filtering algorithms.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.175-178
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• 2003

Two defects have been pointed out in existing user-based collaborative filtering such as sparsity and scalability, and the research has been also made progress, which tries to improve these defects using item-based collaborative filtering. Actually there were many results, but the problem of sparsity still remains because of being based on an explicit data. In addition, the issue has been pointed out. which attributes of item arenot reflected in the recommendation. This paper suggests a recommendation method using nave Bayesian algorithm in hybrid user and item-based collaborative filtering to improve above-mentioned defects of existing item-based collaborative filtering. This method generates a similarity table for each user and item, then it improves the accuracy of prediction and recommendation item using naive Bayesianalgorithm. It was compared and evaluated with existing item-based collaborative filtering technique to estimate the accuracy.

• 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.

• Journal of information and communication convergence engineering
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• v.17 no.2
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• pp.135-141
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• 2019

Collaborative filtering algorithms often encounter data sparsity issues. To overcome this issue, auxiliary information of relevant items is analyzed and an item attribute matrix is derived. In this study, we combine the user-item attribute preference with the traditional similarity calculation method to develop an improved similarity calculation approach and use weights to control the importance of these two elements. A collaborative filtering algorithm based on user-item attribute preference is proposed. The experimental results show that the performance of the recommender system is the most optimal when the weight of traditional similarity is equal to that of user-item attribute preference similarity. Although the rating-matrix is sparse, better recommendation results can be obtained by adding a suitable proportion of user-item attribute preference similarity. Moreover, the mean absolute error of the proposed approach is less than that of two traditional collaborative filtering algorithms.

• 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 the Korea Society of Computer and Information
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• v.23 no.4
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• pp.89-95
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• 2018

It is not easy for the user to find the information that is appropriate for the user among the suddenly increasing information in recent years. One of the ways to help individuals make decisions in such a lot of information is the recommendation system. Although there are many recommendation methods for such recommendation systems, a representative method is collaborative filtering. In this paper, we design and implement the movie recommendation system on user-based collaborative filtering of apache mahout based on mongoDB. In addition, Pearson correlation coefficient is used as a method of measuring the similarity between users. We evaluate Precision and Recall using the MovieLens 100k dataset for performance evaluation.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.273-280
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• 2003

Recommender System can help users to find products to Purchase. A representative method for recommender systems is collaborative filtering (CF). It predict products that user may like based on a group of similar users. User information is based on user's ratings for products and similarities of users are measured by ratings. As user is increasing tremendously, the performance of the pure collaborative filtering is lowed because of high dimensionality and scarcity of data. We consider the effect of dimension deduction in collaborative filtering to cope with scarcity of data experimentally. We suggest that SVD improves the performance of collaborative filtering in comparison with pure collaborative filtering.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.6
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• pp.684-695
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• 2001

Recent recommender system uses a method of combining collaborative filtering system and content based filtering system in order to solve sparsity and first rater problem in collaborative filtering system. Collaborative filtering systems use a database about user preferences to predict additional topics. Content based filtering systems provide recommendations by matching user interests with topic attributes. In this paper, we describe a method for discovery of user preference through combining two techniques for recommendation that allows the application of machine learning algorithm. The proposed collaborative filtering method clusters user using genetic algorithm based on items categorized by Naive Bayes classifier and the content based filtering method builds user profile through extracting user interest using relevance feedback. We evaluate our method on a large database of user ratings for web document and it significantly outperforms previously proposed methods.