• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.6
• /
• pp.2963-2978
• /
• 2017

The Top-K query is currently played a key role in a wide range of road network, decision making and quantitative financial research. In this paper, a Top-K recommendation algorithm is proposed to solve the cold-start problem and a tag generating method is put forward to enhance the semantic understanding of the OLAP session. In addition, a recommendation system for OLAP sessions called "OLAP4R" is designed using collaborative filtering technique aiming at guiding the user to find the ultimate goals by interactive queries. OLAP4R utilizes a mixed system architecture consisting of multiple functional modules, which have a high extension capability to support additional functions. This system structure allows the user to configure multi-dimensional hierarchies and desirable measures to analyze the specific requirement and gives recommendations with forthright responses. Experimental results show that our method has raised 20% recall of the recommendations comparing the traditional collaborative filtering and a visualization tag of the recommended sessions will be provided with modified changes for the user to understand.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.7
• /
• pp.1487-1494
• /
• 2011

In this paper, a novel genre classification algorithm is proposed for music recommendation system. Especially, to improve the classification accuracy, the band-pass filter for octave-based spectral contrast (OSC) feature is designed considering the psycho-acoustic model and actual frequency range of musical instruments. The GTZAN database including 10 genres was used for 10-fold cross validation experiments. The proposed multiple-octave based OSC produces better accuracy by 2.26% compared with the conventional OSC. The combined feature vector based on the proposed OSC and mel-frequency cepstral coefficient (MFCC) gives even better accuracy.

• KIISE Transactions on Computing Practices
• /
• v.22 no.10
• /
• pp.504-512
• /
• 2016

In this paper, we propose a personalized event recommendation scheme using user's activity analysis and collaborative filtering in social network environments. The proposed scheme predicts un-evaluated attribute values through analysis of user activities, relationships, and collaborative filtering. The proposed scheme also incorporates a user's recent preferences by considering the recent history for the user or context-aware information to precisely grasp the user's preferences. As a result, the proposed scheme can recommend events to users with a high possibility to participate in new events, preventing indiscriminate recommendations. In order to show the superiority of the proposed scheme, we compare it with the existing scheme through performance evaluation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.05a
• /
• pp.75-78
• /
• 2018

Recently, the amount of music that can be heard is increasing exponentially due to the growth of the digital music market. Because of this, online music service users have had difficulty choosing their favorite music and have wasted a lot of time. In this paper, we propose a recommendation technique to minimize the difficulty of selection and to reduce wasted time. The proposed technique uses an item - based collaborative filtering algorithm that can recommend items without using personal information. For more accurate recommendation, the user's preference is predicted by using the metadata of the music source and the top-N music with high preference is finally recommended. Experimental results show that the proposed method improves the performance of the proposed method better than it does when the metadata is not used.

• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.5
• /
• pp.390-394
• /
• 2009

The semantic web-based social network is highly useful in a variety of areas. In this paper we make diverse analyses of the FOAF-based social network, and propose an expert recommendation system. This system presents useful method of ontology-based social network using SparQL, RDFS inference, and visualization tools. Then we apply it to real social network in order to make various analyses of centrality, small world, scale free, etc. Moreover, our system suggests method for analysis of an expert on specific field. We expect such method to be utilized in multifarious areas - marketing, group administration, knowledge management system, and so on.

• The Journal of Society for e-Business Studies
• /
• v.9 no.3
• /
• pp.191-205
• /
• 2004

In banking industry, as competition of retail finance is intensive, there appeared eCRM which is a kind of marketing strategy, not only screening good customers but also doing one-to-one marketing for improving customer royalty and satisfaction of goods and service of bank. The strategic importance of eCRM is increasing more and more. We studied bank to use the advantage of expert system and eCRM. The expert system is used as a tool to analyze eCRM. In this paper, first, we presented an eCRM system architecture using expert systems in banking business. Secondly, a bank financial goods recommendation system using expert system is designed and developed. Customers will receive the good quality financial services from the expert system and banks also will acquire the good quality customer data from the expert system.

• Journal of Information Technology Services
• /
• v.13 no.3
• /
• pp.309-321
• /
• 2014

Recently, due to the advent of ubiquitous computing and the spread of intelligent portable device such as smart phone, iPad and PDA has been amplified, a variety of services and the amount of information has also increased fastly. It is becoming a part of our common life style that the demands for enjoying the wireless internet are increasing anytime or anyplace without any restriction of time and place. And also, the demands for e-commerce and many different items on e-commerce and interesting of associated items are increasing. Existing collaborative filtering (CF), explicit method, can not only reflect exact attributes of item, but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. In this paper, using a implicit method without onerous question and answer to the users, not used user's profile for rating to reduce customers' searching effort to find out the items with high purchasability, it is necessary for us to analyse the segmentation of customer and item based on customer data and purchase history data, which is able to reflect the attributes of the item in order to improve the accuracy of recommendation. We propose the method of recommendation system using association rule and weighted preference so as to consider many different items on e-commerce and to refect the profit/weight/importance of attributed of a item. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.5
• /
• pp.609-617
• /
• 2011

This paper proposes a personalized food recommendation system using user profiles auto-generated from Twitter. The user profiles are generated by extracting nouns from Twitter, and calculating emotional scores according to whether each noun is collocated with emotion words. Representative noun information for each food is constructed by analyzing web pages relevant to foods. Appropriate foods for users can be recommended by calculating similarities among the extracted resources. The proposed system has an advantage in that it can always recommend foods even if a user is a newcomer.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.7
• /
• pp.1628-1633
• /
• 2014

In this paper, we propose a news article recommendation system that reflects users' areas of interest and reading habits. Users can select interesting subject then our proposed system displays interesting articles above the other articles. Also the proposed system reflects users' dynamic interests using analyse of user's reading habits. The method of dynamic interest applies the different weight values from users simply clicking and reading entire articles. When users read articles from specific areas, the prosed system increases the weight of these specific areas using XML structure information. Hence users can read their articles of interest with ease.

• Journal of Korean Institute of Industrial Engineers
• /
• v.40 no.1
• /
• pp.128-134
• /
• 2014

With the development of Information Communication Technology and a way of solving problems such as productivity improvement, advanced working attitude, greenhouse gas reduction, low birth rate, aged problem, etc., the need of smart work is increasing. Getting out of legacy working types that are limited in time and space, the flexible seat system at a smart work center that supports the smart working environment is proposed. However, the seat choice based on employee's emotional judgement may cause the decline of forming cooperative space and mutual communication because of the scattered seat arrangement between employees. This paper proposed a seat recommendation method for the mutual cooperation at a smart work center. The method is based on a work similarity of employees. A prototype system also implemented to show the feasibility.