• Journal of the Korea Society of Computer and Information
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• v.13 no.2
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• pp.41-50
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• 2008

This paper proposes the recommendation system which is a new method using RFM method in mobile internet environment. Using a implict method which is not used user's profile for rating, is not used complicated query processing of the request and the response for rating, it is necessary for user to keep the RFM score about users and items based on the whole purchased data in order to recommend the items. As there are some problems which didn't exactly recommend the items with high purchasablity for new customer and new item that do not have the purchase history data. in existing recommendation systems, this proposing system is possible to solve existing problems, and also this system can avoid the duplicated recommendation by the cross comparison with the purchase history data. It can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic cyber shopping mall. Finally, it is able to realize the personalized recommendation system with high purchasablity for one to one web marketing through the mobile internet.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.3
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• pp.138-144
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• 2019

In a modern society, people are concerned seriously about their travel destinations depending on time, economic problem. In this paper, we propose an non-hierarchical clustering based hybrid recommendation using context knowledge. The proposed method is personalized way of recommended knowledge about preferred travel places according to the user's location, place, and weather. Based on 14 attributes from the data collected through the survey, users with similar characteristics are grouped using a non-hierarchical clustering based hybrid recommendation. This makes more accurate recommendation by weighting implicit and explicit data. The users can be recommended a preferred travel destination without spending unnecessary time. The performance evaluation uses accuracy, recall, F-measure. The evaluation result was shown 0.636 accuracy, 0.723 recall, and 0.676 F-measure.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.2
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• pp.112-123
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• 2005

The latest E-commerce sites provide personalized services to maximize user satisfaction for Internet user The collaborative filtering is an algorithm for personalized item real-time recommendation. Various supplementary methods are provided for improving the accuracy of prediction and performance. It is important to consider these two things simultaneously to implement a useful recommendation system. However, established studies on collaborative filtering technique deal only with the matter of accuracy improvement and overlook the matter of performance. This study considers representative attribute-neighborhood, recommendation textile set, and similarity grouping that are expected to improve performance to the recommendation agent system. Ultimately, this paper suggests empirical applications to verify the adequacy and the validity on this system with the development of Fashion Design Recommendation Agent System (FDRAS ).

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.1
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• pp.13-21
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• 2019

The tendency of buyers of web information is changing from the cost-effectiveness which emphasizes the performance over the price to the cost-satisfaction which emphasizes the psychological satisfaction of the buyer. In music recommendation, one of the methods to increase psychological satisfaction is to use the music mood. In this paper, a music recommendation method considering the mood tag and the synonyms tag is proposed and, as an intermediate result of the proposed method, mood tags and music pieces are expressed in Thayer's AV space and then their distribution are analyzed. The analysis result shows the distributions of mood tags and the ones of music pieces are similar, which implies that the proposed recommendation method can provide significant results. In the future, the music recommendation performance will be analyzed.

• Journal of Convergence for Information Technology
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• v.9 no.5
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• pp.1-6
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• 2019

As the mobile market expands, a variety of platforms are available to provide multimodal media content. Multimodal media content contains heterogeneous data, accordingly, user requires much time and effort to select preferred content. Therefore, in this paper we propose multimodal media content classification using keyword weighting for recommendation. The proposed method extracts keyword that best represent contents through keyword weighting in text data of multimodal media contents. Based on the extracted data, genre class with subclass are generated and classify appropriate multimodal media contents. In addition, the user's preference evaluation is performed for personalized recommendation, and multimodal content is recommended based on the result of the user's content preference analysis. The performance evaluation verifies that it is superiority of recommendation results through the accuracy and satisfaction. The recommendation accuracy is 74.62% and the satisfaction rate is 69.1%, because it is recommended considering the user's favorite the keyword as well as the genre.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.6
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• pp.237-244
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• 2022

In this paper, we present VAE-based recommendation using online behavior log and purchase history to overcome data sparsity and cold start. To generate a variable for customers' purchase history, embedding and dimensionality reduction are applied to the customers' purchase history. Also, Variational Autoencoders are applied to online behavior and purchase history. A total number of 12 variables are used, and nDCG is chosen for performance evaluation. Our experimental results showed that the proposed VAE-based recommendation outperforms SVD-based recommendation. Also, the generated purchase history variable improves the recommendation performance.

• The KIPS Transactions:PartB
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• v.19B no.3
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• pp.165-176
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• 2012

In recent years, due to rapidly increasing user-created internet contents coupled with the development of community-based websites, the internet resource recommendation systems are attracting attentions of the users. However, most of the systems have failed in properly reflecting users' characteristics and thus they have difficulty in recommending appropriate resources to users. In this paper, we propose an internet resource recommendation method using FOAF and SNA which fully reflects the characteristics of users. In our method, 1) we extract the data about user characteristics and tags using FOAF; 2) we generate graphs representing users, user characteristics and tags after inserting data into 3 matrixes and integrating them; 3) we recommend the appropriate internet resources after selecting common characteristics of the recommended items and Hot tags by analyzing social network. For verification of our proposed method, we implemented our method to establish and analyze an experimental social group. We verified through our experiments that the more users added in the social network, the higher quality of recommendation result we got than the item-based recommendation method. By using the suggested idea in this paper, we can make a more appropriate recommendation of resources to users while effectively retrieving explosively increasing internet resources.

• Journal of Information Management
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• v.41 no.4
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• pp.227-249
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• 2010

Scholarly information has increased tremendously according to the development of IT, especially the Internet. However, simultaneously, people have to spend more time and exert more effort because of information overload. There have been many research efforts in the field of expert systems, data mining, and information retrieval, concerning a system that recommends user-expected information items through presumption. Recently, the hybrid system combining a content-based recommendation system and collaborative filtering or combining recommendation systems in other domains has been developed. In this paper we resolved the problem of the current recommendation system and suggested a new system combining collaborative filtering and Naive Bayes Classification. In this way, we resolved the over-specialization problem through collaborative filtering and lack of assessment information or recommendation of new contents through Naive Bayes Classification. For verification, we applied the new model in NDSL's paper service of KISTI, especially papers from journals about Sitology and Electronics, and witnessed high satisfaction from 4 experimental participants.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.89-103
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• 2019

Recently, various approaches to recommendation systems have been studied in terms of the quality of recommendation system. A recommender system basically aims to provide personalized recommendations to users for specific items. Most of these systems always recommend the most relevant items of users or items. Traditionally, the evaluation of recommender system quality has focused on the various predictive accuracy metrics of these. However, recommender system must be not only accurate but also useful to users. User satisfaction with recommender systems as an evaluation criterion of recommender system is related not only to how accurately the system recommends but also to how much it supports the user's decision making. In particular, highly serendipitous recommendation would help a user to find a surprising and interesting item. Serendipity in this study is defined as a measure of the extent to which the recommended items are both attractive and surprising to the users. Therefore, this paper proposes an application of serendipity measure to recommender systems to evaluate the performance of recommender systems in terms of recommendation system quality. In this study we define relevant or attractive unexpectedness as serendipity measure for assessing recommendation systems. That is, serendipity measure is evaluated as the measure indicating how the recommender system can find unexpected and useful items for users. Our experimental results show that highly serendipitous recommendation such as item-based collaborative filtering method has better performance than the other recommendations, i.e. user-based collaborative filtering method in terms of recommendation system quality.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.265-274
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• 2021

In this paper, we propose a novel recommendation model based on Doc2vec using search keywords and product details. Until now, a lot of prior studies on recommender systems have proposed collaborative filtering (CF) as the main algorithm for recommendation, which uses only structured input data such as customers' purchase history or ratings. However, the use of unstructured data like online customer review in CF may lead to better recommendation. Under this background, we propose to use search keyword data and product detail information, which are seldom used in previous studies, for product recommendation. The proposed model makes recommendation by using CF which simultaneously considers ratings, search keywords and detailed information of the products purchased by customers. To extract quantitative patterns from these unstructured data, Doc2vec is applied. As a result of the experiment, the proposed model was found to outperform the conventional recommendation model. In addition, it was confirmed that search keywords and product details had a significant effect on recommendation. This study has academic significance in that it tries to apply the customers' online behavior information to the recommendation system and that it mitigates the cold start problem, which is one of the critical limitations of CF.