• Journal of Information Processing Systems
• /
• v.15 no.3
• /
• pp.645-654
• /
• 2019

The purpose of this study was to collect and analyze personal bio data and social network services (SNS) data, derive user preference and user life pattern, and propose intuitive and precise user modeling. This study not only tried to conduct eye tracking experiments using various smart devices to be the ground of the recommendation system considering the attribute of smart devices, but also derived classification preference by analyzing eye tracking data of collected bio data and SNS data. In addition, this study intended to combine and analyze preference of the common classification of the two types of data, derive final preference by each smart device, and based on user life pattern extracted from final preference and collected bio data (amount of activity, sleep), draw the similarity between users using Pearson correlation coefficient. Through derivation of preference considering the attribute of smart devices, it could be found that users would be influenced by smart devices. With user modeling using user behavior pattern, eye tracking, and user preference, this study tried to contribute to the research on the recommendation system that should precisely reflect user tendency.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.1
• /
• pp.1-8
• /
• 2009

Learning user's preference is a key issue in intelligent system such as personalized service. The study on user preference model has adapted simple user preference model, which determines a set of preferred keywords or topic, and weights to each target. In this paper, we recommend multi-perspective user preference model that factors sentiment information in the model. Based on the topicality and sentimental information processed using natural language processing techniques, it learns a user's preference. To handle timc-variant nature of user preference, user preference is calculated by session, short-term and long term. User evaluation is used to validate the effect of user preference teaming and it shows 86.52%, 86.28%, 87.22% of accuracy for topic interest, keyword interest, and keyword favorableness.

• Journal of Advanced Navigation Technology
• /
• v.15 no.5
• /
• pp.925-930
• /
• 2011

In this paper, we propose a method to apply the purchase preference of a user in on-line shopping mall. To analyze the preference, we use topic preference vector. The topic is purchase count of products. In this structure, we construct the association the four factors; Purchase Hit meaning the purchase count of product, Count meaning the purchase count by other users in interesting product, Preference meaning product preference, and product meaning information of the product. By this structure and the method, we could show that proposed method displayed the product applying user preference, effectively.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.5 no.7
• /
• pp.1346-1367
• /
• 2011

As the size of camera modules is decreasing and as the computing performance of portable devices is improving, taking photos has become a part of daily life. However, existing photo management programs and products that manage such photos still require extensive user effort to facilitate the sharing and browsing of images. It is especially difficult for novice users to manage and share photos. In this paper, we develop a round-robin concept-driven user preference feedback mechanism for achieving direct photo sharing, instant display, and easy management using optimized user controls and user preference-driven classification. Compared with commercial photo management systems, our proposed solution provides new features: optimized user controls, direct sharing and instant display, and user preference feedback driven classification. These new features boost the round-robin concept-driven user preference feedback. This paper proposes a photo finder that automatically searches for photos in storage spaces or cameras. The proposed photo finder relies on user preference feedback to share photos by leveraging user preferences, and the round-robin connection transmits photos to the family's digital photo frame or web album by arbiter. The proposed method saves time and spares users the effort required for photo management. Moreover, this method does not merely direct photo sharing and simple photo management, but it also increases the satisfaction level of users viewing the photos.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.8 no.1
• /
• pp.119-126
• /
• 2009

In recent years, many recommendation service systems that search or recommend information automatically considering user's taste or property are developed. However, there is a weak point that correct recommendation is hard without considering the preference of user's context. This paper proposes a filtering method that gives correct recommendation considering the preference of user's context. To support this method, we get UCOP(User-Context Object Preference) using the preference of user's context and Pearson correlation coefficient. The results of the experiment show the improvement of 11%, 2% of precision and 8%, 4% of recall comparing with the existing service systems. Our recommendation service systems show 77% of precision and 53% of recall overall.

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

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.8
• /
• pp.141-148
• /
• 2010

In mobile terminal recommend service system has general information restrictive recommend that individuality considering to user's information find and recommend. Also it has difficult of accurate information recommend bad points user's not offer individuality information preference recommend service. Therefore this paper is propose user's information individuality preference considering by user's individuality preference recommendation system using improved k-means algorithm. Propose method is correlation coefficients using user's information individuality preference when user's individuality preference recommendation using improved k-means algorithm. Restrictive information recommend to fix a problem, information of restrictive general recommend that user's information individuality preference offer to accurate information recommend. Performance experiment is existing service system as compared to evaluating the effectiveness of precision and recall, performance experiment result is appear to precision 85%, recall 68%.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.5
• /
• pp.61-69
• /
• 2014

Collaborative filtering recommendation creates similar item subset or similar user subset based on user preference about items and predict user preference to particular item by using them. Thus, if preference matrix has low density, reliability of recommendation will be sharply decreased. To solve these problems we suggest Hybrid Preference Prediction Technique Using Weighting based Data Reliability. Preference prediction is carried out by creating similar item subset and similar user subset and predicting user preference by each subset and merging each predictive value by weighting point applying model condition. According to this technique, we can increase accuracy of user preference prediction and implement recommendation system which can provide highly reliable recommendation when density of preference matrix is low. Efficiency of this system is verified by Mean Absolute Error. Proposed technique shows average 21.7% improvement than Hao Ji's technique when preference matrix sparsity is more than 84% through experiment.

• Proceedings of the IEEK Conference
• /
• 2008.06a
• /
• pp.641-642
• /
• 2008

In this paper, we propose a new approach for the implicit rating algorithm of finding user's intense and preference to the contents on the web. Although the explicit method dig out the user preference of specific contents based on the user's intervention, we propose the implicit method obtaining the user preference according to the user's behavioral patterns on the web implicitly and automatically without the user's intervention. The implementation results show that the proposed approach is highly valuable for supporting recommender systems in conjunction with the users lifestyle.

• Journal of the Korean Data and Information Science Society
• /
• v.17 no.3
• /
• pp.717-726
• /
• 2006

This study is to investigate the MAE of prediction value by collaborative filtering algorithm originated by GroupLens and improved algorithm. To decrease the MAE on the collaborative recommender system on user based, this research proposes the improved algorithm, which reduces the possibility of over estimation of active user's preference mean collaboratively using other user’s preference mean. The result shows the MAE of prediction by improved algorithm is better than original algorithm, so the active user's preference mean used in prediction formula is possibly over estimated.