• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.133-140
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• 2012

In this paper, we propose a music recommendation method considering users' tags by collaborative tagging in a social music site. Since collaborative tagging allows a user to add keywords chosen by himself to web resources, it provides users' preference about the web resources concretely. In particular, emotional tags which represent human's emotion contain users' musical preference more directly than factual tags which represent facts such as musical genre and artists. Therefore, to classify the tags into the emotional tags and the factual tags and to assign weighted values to the emotional tags, a tag ontology called UniTag is developed. After preprocessing the tags, the weighted tags are used to create user profiles, and the music recommendation algorithm is executed based on the profiles. To evaluate the proposed method, a conventional playcount-based recommendation, an unweighted tag-based recommendation, and an weighted tag-based recommendation are executed. Our experimental results show that the weighted tag-based recommendation outperforms other two approaches in terms of precision.

• Information Systems Review
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• v.19 no.2
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• pp.71-94
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• 2017

Recommender systems reduce information overload and enhance choice quality. This technology is used in many services and industry. Previous studies did not consider recommendation quantity and the repetitive recommendations of an item. This study is the first to examine recommender systems by considering recommendation quantity and repetitive recommendations. Only a limited number of items are displayed in offline stores because of their physical limitations. Determining the type and number of items that will be displayed is an important consideration. In this study, I suggest the use of a user-based recommender system that can recommend the most appropriate items for each store. This model is evaluated by MAE, Precision, Recall, and F1 measure, and shows higher performance than the baseline model. I also suggest a new performance evaluation measure that includes Quantity Precision, Quantity Recall, and Quantity F1 measure. This measure considers the penalty for short or excess recommendation quantity. Novelty is defined as the proportion of items in a recommendation list that consumers may not experience. I evaluate the new revenue creation effect of the suggested model using this novelty measure. Previous research focused on recommendations for customer online, but I expand the recommender system to cover stores offline.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.238-252
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• 2009

Due to the rapid increase of available contents via the convergence of broadcasting and internet, the efficient access to personally preferred contents has become an important issue. In this paper, for recommendation scheme for TV programs using a collaborative filtering technique is studied. For recommendation of user preferred TV programs, our proposed recommendation scheme consists of offline and online computation. About offline computation, we propose reasoning implicitly each user's preference in TV programs in terms of program contents, genres and channels, and propose clustering users based on each user's preferences in terms of genres and channels by dynamic fuzzy clustering method. After an active user logs in, to recommend TV programs to the user with high accuracy, the online computation includes pulling similar users to an active user by similarity measure based on the standard preference list of active user and filtering-out of the watched TV programs of the similar users, which do not exist in EPG and ranking of the remaining TV programs by proposed rank model. Especially, in this paper, the BM (Best Match) algorithm is extended to make the recommended TV programs be ranked by taking into account user's preferences. The experimental results show that the proposed scheme with the extended BM model yields 62.1% of prediction accuracy in top five recommendations for the TV watching history of 2,441 people.

• KIPS Transactions on Computer and Communication Systems
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• v.13 no.1
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• pp.10-20
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• 2024

As the online commerce market continues to expand with an increase of diverse products and content, users find it challenging in navigating and in the selection process. Thereafter both platforms and shopping malls are actively working in conducting continuous research on recommendations system to select and present products that align with user preferences. Most existing recommendation studies have relied on user data which is relatively easy to obtain. However, these studies only use a single type of event and their reliance on time dependent data results in issues with reliability and complexity. To address these challenges, this paper proposes a recommendation system that analysis user preferences in consideration of the relationship between various types of event data. The proposed recommendation system analyzes the correlation of multiple events, extracts weights, learns the recommendation model, and provides recommendation services through it. Through extensive experiments the performance of our system was compared with the previously studied algorithms. The results confirmed an improvement in both complexity and performance.

• Journal of Korea Multimedia Society
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• v.10 no.1
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• pp.128-138
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• 2007

With the development of broadcasting technology from analogue to interactive digital, the number of TV channels and contents provided to audience is increasing in a rapid speed. In this multi-media and multi-channel world, it is difficult to adapt to the increase of TV channel numbers and their contents merely using remote controller to search channels. Due to this reason, EPG (Electronic Program Guide) has been one of the essential services providing convenience to audience. So EPG complying with European DVB-MHP specifications, which will be also our domestic standard, is proposed in this paper. In order to provide audiences with DiTV contents they preferred, we apply collaborative filtering algorithm to recommend contents according to preference value of audience group with similar preference. And we use JavaXlet application which is based on MHP to implement this EPG, while the result can be verified by OpenMHP emulator.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.282-289
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• 2008

Although there were some technological developments in improving the collaborative filtering, they have yet to fully reflect the actual relation of the items. In this paper, we propose the recommendation system using associative web document classification by word frequency and ${\alpha}$-cut to address the short comings of the collaborative filtering. The proposed method extracts words from web documents through the morpheme analysis and accumulates the weight of term frequency. It makes associative rules and applies the weight of term frequency to its confidence by using Apriori algorithm. And it calculates the similarity among the words using the hypergraph partition. Lastly, it classifies related web document by using ${\alpha}$-cut and calculates similarity by using adjusted cosine similarity. The results show that the proposed method significantly outperforms the existing methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.316-318
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• 2019

Recently, healthy life has become an issue in an aging society, and the number of people who have been interested in continuous health care for better life is increasing. In this paper, we implemented a personalized recommendation systm to provide convenient healthcare management for user. The PHR (Personal Health Record) of user could be stored in the server along with health related information such as lifestyle, disease, and physical condition. The users could be classified into similar clusters according to the PHR profile in order to provide healthcare contents to the users who had similar PHR profile. K-Means clustering was applied to generate clusters based on PHR profile and ACDT(Ant Colony Decision Tree) algorithm was used to provide personalised recommendation of health information stored in knowledge base. The app system developed in this paper is useful for users to perform healthcare themselves by providing information on serious diseases and lifestyle habits to be improved according to the clusters classified by PHR profile.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.12
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• pp.5113-5119
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• 2010

Anthropometry has been broadly explored in various fields including automobile industry, home electronic appliances, medical appliances and sports goods with aiming at reaching satisfaction to consumer's need and efficiency. However, current technologies to measure a human body still have barriers in which the methods mostly seem to be contingent on expensive devices such as scanner and digital measuring instruments and to be directly touchable to the body when obtaining body size.. Therefore, in this paper, we present a general method to automatically extract size of body from a real body image acquired from a camera and to utilize it into recommend systems including clothing and bicycle fitting. At first, Haar-like features and AdaBoost algorithm are employed to detect body position. Then features of body can be recognized using AAM. Finally clothing and bicycle recommending modules have been implemented and experimented to validate the proposed method.

• Journal of Digital Contents Society
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• v.19 no.5
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• pp.871-880
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• 2018

Recently, the technology of recommendation of POI (Point of Interest) related technology is getting attention with the increase of big data related to consumers. Previous studies on POI recommendation systems have been limited to specific data sets. The problem is that if the study is carried out with this particular dataset, it may be suitable for the particular dataset. Therefore, this study analyzes the similarity and correlation between stores using the user visit data obtained from the integrated sensor installed in Seoul and Songjeong roads. Based on the results of the analysis, we study the preference prediction system which recommends the stores that new users are interested in. As a result of the experiment, various similarity and correlation analysis were carried out to obtain a list of relevant stores and a list of stores with low relevance. In addition, we performed a comparative experiment on the preference prediction accuracy under various conditions. As a result, it was confirmed that the jacquard similarity based item collaboration filtering method has higher accuracy than other methods.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.175-186
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• 2021

The roles and functions of domestic public libraries are diversifying, but various problems have emerged due to internally biased book lending. In addition, due to the 4th Industrial Revolution, public libraries have introduced a book recommendation system focusing on popular books, but the variety of books that users can access is limited. Therefore, in this study, the public library unborrowed book recommendation system was implemented limiting its spatial scope to Duryu Library in Daegu City to enhance the satisfaction of public library users, by using the loan records data (213,093 cases), user information (35,561 people), etc. and utilizing methods like cluster analysis, topic modeling, content-based filtering recommendation algorithm, and conducted a survey on actual users' satisfaction to present the possibility and implications of the unborrowed book recommendation system. As a result of the analysis, the majority of users responded with high satisfaction, and was able to find the satisfaction was relatively high in the class classified by specific gender, age, occupation, and usual reading. Through the results of this study, it is expected that some problems such as biased book lending and reduced operational efficiency of public libraries can be improved, and limitations of the study was also presented.