Go to the main menu
Skip to content
Go to bottom
REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
> Journal Vol & Issue
The KIPS Transactions:PartB
Journal Basic Information
Journal DOI :
Korea Information Processing Society
Editor in Chief :
Volume & Issues
Volume 19B, Issue 4 - Aug 2012
Volume 19B, Issue 3 - Jun 2012
Volume 19B, Issue 2 - Apr 2012
Volume 19B, Issue 1 - Feb 2012
Selecting the target year
Multidimensional Optimization Model of Music Recommender Systems
Park, Kyong-Su ; Moon, Nam-Me ;
The KIPS Transactions:PartB, volume 19B, issue 3, 2012, Pages 155~164
DOI : 10.3745/KIPSTB.2012.19B.3.155
This study aims to identify the multidimensional variables and sub-variables and study their relative weight in music recommender systems when maximizing the rating function R. To undertake the task, a optimization formula and variables for a research model were derived from the review of prior works on recommender systems, which were then used to establish the research model for an empirical test. With the research model and the actual log data of real customers obtained from an on line music provider in Korea, multiple regression analysis was conducted to induce the optimal correlation of variables in the multidimensional model. The results showed that the correlation value against the rating function R for Items was highest, followed by Social Relations, Users and Contexts. Among sub-variables, popular music from Social Relations, genre, latest music and favourite artist from Items were high in the correlation with the rating function R. Meantime, the derived multidimensional recommender systems revealed that in a comparative analysis, it outperformed two dimensions(Users, Items) and three dimensions(Users, Items and Contexts, or Users, items and Social Relations) based recommender systems in terms of adjusted
and the correlation of all variables against the values of the rating function R.
Improved Internet Resource Recommendation Method using FOAF and SNA
Wang, Qing ; Sohn, Jong-Soo ; Chung, In-Jeong ;
The KIPS Transactions:PartB, volume 19B, issue 3, 2012, Pages 165~176
DOI : 10.3745/KIPSTB.2012.19B.3.165
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.
Gaussian Interpolation-Based Pedestrian Tracking in Continuous Free Spaces
Kim, In-Cheol ; Choi, Eun-Mi ; Oh, Hui-Kyung ;
The KIPS Transactions:PartB, volume 19B, issue 3, 2012, Pages 177~182
DOI : 10.3745/KIPSTB.2012.19B.3.177
We propose effective motion and observation models for the position of a WiFi-equipped smartphone user in large indoor environments. Three component motion models provide better proposal distribution of the pedestrian`s motion. Our Gaussian interpolation-based observation model can generate likelihoods at locations for which no calibration data is available. These models being incorporated into the particle filter framework, our WiFi fingerprint-based localization algorithm can track the position of a smartphone user accurately in large indoor environments. Experiments carried with an Android smartphone in a multi-story building illustrate the performance of our WiFi localization algorithm.
Retrieval Model Based on Word Translation Probabilities and the Degree of Association of Query Concept
Kim, Jun-Gil ; Lee, Kyung-Soon ;
The KIPS Transactions:PartB, volume 19B, issue 3, 2012, Pages 183~188
DOI : 10.3745/KIPSTB.2012.19B.3.183
One of the major challenge for retrieval performance is the word mismatch between user`s queries and documents in information retrieval. To solve the word mismatch problem, we propose a retrieval model based on the degree of association of query concept and word translation probabilities in translation-based model. The word translation probabilities are calculated based on the set of a sentence and its succeeding sentence pair. To validate the proposed method, we experimented on TREC AP test collection. The experimental results show that the proposed model achieved significant improvement over the language model and outperformed translation-based language model.
Query Expansion Based on Word Graphs Using Pseudo Non-Relevant Documents and Term Proximity
Jo, Seung-Hyeon ; Lee, Kyung-Soon ;
The KIPS Transactions:PartB, volume 19B, issue 3, 2012, Pages 189~194
DOI : 10.3745/KIPSTB.2012.19B.3.189
In this paper, we propose a query expansion method based on word graphs using pseudo-relevant and pseudo non-relevant documents to achieve performance improvement in information retrieval. The initially retrieved documents are classified into a core cluster when a document includes core query terms extracted by query term combinations and the degree of query term proximity. Otherwise, documents are classified into a non-core cluster. The documents that belong to a core query cluster can be seen as pseudo-relevant documents, and the documents that belong to a non-core cluster can be seen as pseudo non-relevant documents. Each cluster is represented as a graph which has nodes and edges. Each node represents a term and each edge represents proximity between the term and a query term. The term weight is calculated by subtracting the term weight in the non-core cluster graph from the term weight in the core cluster graph. It means that a term with a high weight in a non-core cluster graph should not be considered as an expanded term. Expansion terms are selected according to the term weights. Experimental results on TREC WT10g test collection show that the proposed method achieves 9.4% improvement over the language model in mean average precision.
Morpheme Recovery Based on Naïve Bayes Model
Kim, Jae-Hoon ; Jeon, Kil-Ho ;
The KIPS Transactions:PartB, volume 19B, issue 3, 2012, Pages 195~200
DOI : 10.3745/KIPSTB.2012.19B.3.195
In Korean, spelling change in various forms must be recovered into base forms in morphological analysis as well as part-of-speech (POS) tagging is difficult without morphological analysis because Korean is agglutinative. This is one of notorious problems in Korean morphological analysis and has been solved by morpheme recovery rules, which generate morphological ambiguity resolved by POS tagging. In this paper, we propose a morpheme recovery scheme based on machine learning methods like Na
ve Bayes models. Input features of the models are the surrounding context of the syllable which the spelling change is occurred and categories of the models are the recovered syllables. The POS tagging system with the proposed model has demonstrated the
-score of 97.5% for the ETRI tree-tagged corpus. Thus it can be decided that the proposed model is very useful to handle morpheme recovery in Korean.
Automatic Identification of the Lumen Border in Intravascular Ultrasound Images
Park, Jun-Oh ; Ko, Byoung-Chul ; Park, Hee-Jun ; Nam, Jae-Yeal ;
The KIPS Transactions:PartB, volume 19B, issue 3, 2012, Pages 201~208
DOI : 10.3745/KIPSTB.2012.19B.3.201
Accurately segmenting lumen border in intravascular ultrasound images (IVUS) is very important to study vascular wall architecture for diagnosis of the cardiovascular diseases. After each of IVUS image is transformed to a polar coordinated image, initial points are detected using wavelet transform. Then, lumen border is initialized as the set of important points using non parametric probability density function and smoothing function by removing outlier initial points occurred by noises and artifacts. Finally, polynomial curve fitting is applied to obtain real lumen border using filtered important points. The evaluation of proposed method was performed with related method and the proposed method produced accurate lumen contour detection when compared to another method in most types of IVUS images.