Loading [MathJax]/jax/output/CommonHTML/jax.js
  • Title/Summary/Keyword: 실험기법

Search Result 15,528, Processing Time 0.043 seconds

Studies on the Improvement of Utility Value of Corn Grains by Different Processing Methods I. Effects of Different Corn Processing Methods on In situ and In vitro Digestibilities in Hanwoo (옥수수 알곡의 가공처리에 의한 영양소 이용성 향상에 관한 연구 I. 한우에 있어서 옥수수 알곡의 가공처리가 In situ 및 In vitro 소화율에 미치는 영향)

  • Kim, W.Y;Kim, H.W.;Lee, J.H.
    • Journal of Practical Agriculture & Fisheries Research
    • /
    • v.3 no.1
    • /
    • pp.116-131
    • /
    • 2001
  • Experiments were conducted to determine effects of whole and processed corns on in situ disappearance rates of nutrients in the rumen and in vitro degradability of dry matter by rumen microorganisms. Whole corn(WC) was processed into four different types; ground corn(GC), cracked corn(CC), flaked corn(FC), and soaked corn(SC). In the in situ experiment, the rate of ruminal DM disappearance after 48 hour incubation was highest in GC(76.1%) and lowest in WC and SC(12% ; P<0.01). The rate of ruminal CP disappearance after 48 hour incubation was highest in GC and CC(48 and 38%, respectively; P<0.01). The rate of ruminal OM disappearance after 48 hour incubation was highest in GC(76.14%) and lowest in WC and SC(11.82 and 12.26%, respectively; P<0.01). In the in vitro experiment, the two-stage incubation technique was used to measure digestibilities of whole and processed corns. The digestibility of DM was higher in GC, CC and FC(86.95, 85.84 and 82.29%, respectively) than in WC(15.36%; P<0.01).

Analysis of the Effects of E-commerce User Ratings and Review Helfulness on Performance Improvement of Product Recommender System (E-커머스 사용자의 평점과 리뷰 유용성이 상품 추천 시스템의 성능 향상에 미치는 영향 분석)

  • FAN, LIU;Lee, Byunghyun;Choi, Ilyoung;Jeong, Jaeho;Kim, Jaekyeong
    • Journal of Intelligence and Information Systems
    • /
    • v.28 no.1
    • /
    • pp.311-328
    • /
    • 2022
  • Because of the spread of smartphones due to the development of information and communication technology, online shopping mall services can be used on computers and mobile devices. As a result, the number of users using the online shopping mall service increases rapidly, and the types of products traded are also growing. Therefore, to maximize profits, companies need to provide information that may interest users. To this end, the recommendation system presents necessary information or products to the user based on the user's past behavioral data or behavioral purchase records. Representative overseas companies that currently provide recommendation services include Netflix, Amazon, and YouTube. These companies support users' purchase decisions by recommending products to users using ratings, purchase records, and clickstream data that users give to the items. In addition, users refer to the ratings left by other users about the product before buying a product. Most users tend to provide ratings only to products they are satisfied with, and the higher the rating, the higher the purchase intention. And recently, e-commerce sites have provided users with the ability to vote on whether product reviews are helpful. Through this, the user makes a purchase decision by referring to reviews and ratings of products judged to be beneficial. Therefore, in this study, the correlation between the product rating and the helpful information of the review is identified. The valuable data of the evaluation is reflected in the recommendation system to check the recommendation performance. In addition, we want to compare the results of skipping all the ratings in the traditional collaborative filtering technique with the recommended performance results that reflect only the 4 and 5 ratings. For this purpose, electronic product data collected from Amazon was used in this study, and the experimental results confirmed a correlation between ratings and review usefulness information. In addition, as a result of comparing the recommendation performance by reflecting all the ratings and only the 4 and 5 points in the recommendation system, the recommendation performance of remembering only the 4 and 5 points in the recommendation system was higher. In addition, as a result of reflecting review usefulness information in the recommendation system, it was confirmed that the more valuable the review, the higher the recommendation performance. Therefore, these experimental results are expected to improve the performance of personalized recommendation services in the future and provide implications for e-commerce sites.

Research on ITB Contract Terms Classification Model for Risk Management in EPC Projects: Deep Learning-Based PLM Ensemble Techniques (EPC 프로젝트의 위험 관리를 위한 ITB 문서 조항 분류 모델 연구: 딥러닝 기반 PLM 앙상블 기법 활용)

  • Hyunsang Lee;Wonseok Lee;Bogeun Jo;Heejun Lee;Sangjin Oh;Sangwoo You;Maru Nam;Hyunsik Lee
    • KIPS Transactions on Software and Data Engineering
    • /
    • v.12 no.11
    • /
    • pp.471-480
    • /
    • 2023
  • The Korean construction order volume in South Korea grew significantly from 91.3 trillion won in public orders in 2013 to a total of 212 trillion won in 2021, particularly in the private sector. As the size of the domestic and overseas markets grew, the scale and complexity of EPC (Engineering, Procurement, Construction) projects increased, and risk management of project management and ITB (Invitation to Bid) documents became a critical issue. The time granted to actual construction companies in the bidding process following the EPC project award is not only limited, but also extremely challenging to review all the risk terms in the ITB document due to manpower and cost issues. Previous research attempted to categorize the risk terms in EPC contract documents and detect them based on AI, but there were limitations to practical use due to problems related to data, such as the limit of labeled data utilization and class imbalance. Therefore, this study aims to develop an AI model that can categorize the contract terms based on the FIDIC Yellow 2017(Federation Internationale Des Ingenieurs-Conseils Contract terms) standard in detail, rather than defining and classifying risk terms like previous research. A multi-text classification function is necessary because the contract terms that need to be reviewed in detail may vary depending on the scale and type of the project. To enhance the performance of the multi-text classification model, we developed the ELECTRA PLM (Pre-trained Language Model) capable of efficiently learning the context of text data from the pre-training stage, and conducted a four-step experiment to validate the performance of the model. As a result, the ensemble version of the self-developed ITB-ELECTRA model and Legal-BERT achieved the best performance with a weighted average F1-Score of 76% in the classification of 57 contract terms.

Optimization of Test Parameters and Interlaboratory Validation of Neuro-2a Assay for the Detection of Tetrodotoxin (테트로도톡신 검출을 위한 Neuro-2a 시험법의 시험 매개변수 최적화 및 실험실 간 검증 연구)

  • Jeong-In Park;Jun Kim;Si-Yun Hong;Youngjin Kim;Hyun Park;Young-Seok Han;Youn-Jung Kim
    • Journal of Food Hygiene and Safety
    • /
    • v.39 no.5
    • /
    • pp.412-421
    • /
    • 2024
  • Tetrodotoxin (TTX) is a potent marine-derived neurotoxin. Existing detection methods for TTX, such as mouse bioassay (MBA) and LC-MS/MS, are limited by ethical concerns, and low detection thresholds, particularly in the absence of reference standards. Alternative testing methods are thus critically needed. The Neuro-2a assay is a well-established cell-based assay that uses mouse-derived Neuro-2a cells treated with ouabain (O) and veratridine (V), to induce cell death via excessive Na+ influx. This assay quantitates TTX based on its ability to inhibit Na+ influx, thereby allowing the cells to survive. In this study, we optimized parameters including TTX treatment conditions and O/V concentrations, to adapt the Neuro-2a assay for domestic laboratory conditions. The optimal O/V concentrations were determined to be 600/60 µM. We also identified eight concentration points (50-0.195 ng/mL) that generated a sigmoidal dose-response curve. By conducting 24 replicate experiments, we established six key data criteria to ensure reliability, with EC50 values ranging from 3.824 to 1.268 ng/mL. A comparison of inter-laboratory variability revealed that all quality control and data criteria values, except for COV+ and Bottom OD, showed coefficients of variation (CVs) ranging from 1.31 to 14.92%, confirming the assay's accuracy and reproducibility. In this study, we also elucidate optimal assay conditions, validated quality control and data criteria for TTX detection using the Neuro-2a assay in local laboratories. Additionally, we conducted an assay with 4,9-anhydroTTX, a TTX analog, and determined a TEF value of 0.2098, demonstrating the applicability of this method for detecting TTX and its analogs. This optimized Neuro-2a assay is expected to serve as an effective alternative to MBA for detecting TTX in domestic seafood products.

The Study about Application of LEAP Collimator at Brain Diamox Perfusion Tomography Applied Flash 3D Reconstruction: One Day Subtraction Method (Flash 3D 재구성을 적용한 뇌 혈류 부하 단층 촬영 시 LEAP 검출기의 적용에 관한 연구: One Day Subtraction Method)

  • Choi, Jong-Sook;Jung, Woo-Young;Ryu, Jae-Kwang
    • The Korean Journal of Nuclear Medicine Technology
    • /
    • v.13 no.3
    • /
    • pp.102-109
    • /
    • 2009
  • Purpose: Flash 3D (pixon(R) method; 3D OSEM) was developed as a software program to shorten exam time and improve image quality through reconstruction, it is an image processing method that usefully be applied to nuclear medicine tomography. If perfoming brain diamox perfusion scan by reconstructing subtracted images by Flash 3D with shortened image acquisition time, there was a problem that SNR of subtracted image is lower than basal image. To increase SNR of subtracted image, we use LEAP collimators, and we emphasized on sensitivity of vessel dilatation than resolution of brain vessel. In this study, our purpose is to confirm possibility of application of LEAP collimators at brain diamox perfusion tomography, identify proper reconstruction factors by using Flash 3D. Materials and methods: (1) The evaluation of phantom: We used Hoffman 3D Brain Phantom with 99mTc. We obtained images by LEAP and LEHR collimators (diamox image) and after 6 hours (the half life of 99mTc: 6 hours), we use obtained second image (basal image) by same method. Also, we acquired SNR and ratio of white matters/gray matters of each basal image and subtracted image. (2) The evaluation of patient's image: We quantitatively analyzed patients who were examined by LEAP collimators then was classified as a normal group and who were examined by LEHR collimators then was classified as a normal group from 2008. 05 to 2009. 01. We evaluate the results from phantom by substituting factors. We used one-day protocol and injected 99mTc-ECD 925 MBq at both basal image acquisition and diamox image acquisition. Results: (1) The evaluation of phantom: After measuring counts from each detector, at basal image 41~46 kcount, stress image 79~90 kcount, subtraction image 40~47 kcount were detected. LEAP was about 102~113 kcount at basal image, 188~210 kcount at stress image and 94~103 at subtraction image kcount were detected. The SNR of LEHR subtraction image was decreased than LEHR basal image about 37%, the SNR of LEAP subtraction image was decreased than LEAP basal image about 17%. The ratio of gray matter versus white matter is 2.2:1 at LEHR basal image and 1.9:1 at subtraction, and at LEAP basal image was 2.4:1 and subtraction image was 2:1. (2) The evaluation of patient's image: the counts acquired by LEHR collimators are about 40~60 kcounts at basal image, and 80~100 kcount at stress image. It was proper to set FWHM as 7 mm at basal and stress image and 11mm at subtraction image. LEAP was about 80~100 kcount at basal image and 180~200 kcount at stress image. LEAP images could reduce blurring by setting FWHM as 5 mm at basal and stress images and 7 mm at subtraction image. At basal and stress image, LEHR image was superior than LEAP image. But in case of subtraction image like a phantom experiment, it showed rough image because SNR of LEHR image was decreased. On the other hand, in case of subtraction LEAP image was better than LEHR image in SNR and sensitivity. In all LEHR and LEAP collimator images, proper subset and iteration frequency was 8 times. Conclusions: We could archive more clear and high SNR subtraction image by using proper filter with LEAP collimator. In case of applying one day protocol and reconstructing by Flash 3D, we could consider application of LEAP collimator to acquire better subtraction image.

  • PDF

Wearable Art-Chameleon Dress (웨어러블 아트-카멜레온 드레스)

  • Cho, Kyoung-Hee
    • Journal of the Korean Society of Clothing and Textiles
    • /
    • v.32 no.12
    • /
    • pp.1837-1847
    • /
    • 2008
  • The goal of this study is to express the image of chameleons-that change their colors by light, temperature and its mood-into the sexy styles of corresponding coquettish temperamental people in Wearable Art. The method used in this study was experimenting various production mediums, including creating the textured stretch fabric, in the process of expressing the conceptual characteristics of the chameleon in Wearable Art. The concept of the work was a concoction of 'tempting', 'splendid', 'brilliant', 'fascinating', etc. that highlighted the real disposition of the chameleon. The futuristic preference of the researcher was also implicated. "Comfortable" and "enjoyable" concepts via motions were improved with the its completeness. The point of the design and production is to express symbolically the chameleon in real life, analyzing its sleek body lines, conditional colors changing, outer skins and the cubic textures. The coquettish temperamental image, the conceptual image of the chameleon, was also expressed by implication into the whole work. The entire line of this work is body-conscious silhouette. It was symbolically selected to image the outline of the chameleon that has the slim and sleek body. The exposed back is intended to express symbolically the projected back bones of the chameleon. The hood of gentle triangle line expresses the smooth-lined head part. The irregular hemlines represent the elongated chameleon's tale. The chameleon with its colors of vivid tones is characterized the colors changing by its conditions. This point was importantly treated in the working process by trying the effects that the colors are seen slightly different according to the light and angles. The material was given the effect that its surface colors are seen different in lights and angles because of the wrinkles protruded lumpy-bumpy. The various stones of red and blue tones are very similar to the skin tones of the real chameleon, and their gradation makes the effect that the colors are visibly changed with each move. The textures of the chameleon were produced via the wrinkle effect of smoke-shape, which is the result of using the elastic threads on the basic mediums stitched with 50/50 chiffon and polyester along with velvet dot patterns. The stretching fabric by the impact of the elastic threads is as much suitable for making the body-conscious line. The stones are composed of acrylic cabochon and gemstone. They are symbolically expressed the lumpy and bumpy back skin of the chameleon and produced the effect of the colors visibly different. The primary technique used in this dress is the draping utilizing the biased grains. The front body piece is connected to the hood and joined to the back piece without any seam. For the irregular hemline flares, leaving the several rectangular pieces with bias grains, they were connected by interlocking. What defines the clothes is the person in action. Therefore, what decides the completeness of clothes might be its comfortable and enjoyable feeling by living and acting people. The chameleon dress could also reach its goal of comforting and pleasing Wearable Art in the process of studying the techniques and effects that visibly differentiate the colors. It is considered as a main point of the Wearable Art, which is a comfortable enjoyable clothing tempered with the artistic beauty.

A Hybrid Recommender System based on Collaborative Filtering with Selective Use of Overall and Multicriteria Ratings (종합 평점과 다기준 평점을 선택적으로 활용하는 협업필터링 기반 하이브리드 추천 시스템)

  • Ku, Min Jung;Ahn, Hyunchul
    • Journal of Intelligence and Information Systems
    • /
    • v.24 no.2
    • /
    • pp.85-109
    • /
    • 2018
  • Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.

Automatic Quality Evaluation with Completeness and Succinctness for Text Summarization (완전성과 간결성을 고려한 텍스트 요약 품질의 자동 평가 기법)

  • Ko, Eunjung;Kim, Namgyu
    • Journal of Intelligence and Information Systems
    • /
    • v.24 no.2
    • /
    • pp.125-148
    • /
    • 2018
  • Recently, as the demand for big data analysis increases, cases of analyzing unstructured data and using the results are also increasing. Among the various types of unstructured data, text is used as a means of communicating information in almost all fields. In addition, many analysts are interested in the amount of data is very large and relatively easy to collect compared to other unstructured and structured data. Among the various text analysis applications, document classification which classifies documents into predetermined categories, topic modeling which extracts major topics from a large number of documents, sentimental analysis or opinion mining that identifies emotions or opinions contained in texts, and Text Summarization which summarize the main contents from one document or several documents have been actively studied. Especially, the text summarization technique is actively applied in the business through the news summary service, the privacy policy summary service, ect. In addition, much research has been done in academia in accordance with the extraction approach which provides the main elements of the document selectively and the abstraction approach which extracts the elements of the document and composes new sentences by combining them. However, the technique of evaluating the quality of automatically summarized documents has not made much progress compared to the technique of automatic text summarization. Most of existing studies dealing with the quality evaluation of summarization were carried out manual summarization of document, using them as reference documents, and measuring the similarity between the automatic summary and reference document. Specifically, automatic summarization is performed through various techniques from full text, and comparison with reference document, which is an ideal summary document, is performed for measuring the quality of automatic summarization. Reference documents are provided in two major ways, the most common way is manual summarization, in which a person creates an ideal summary by hand. Since this method requires human intervention in the process of preparing the summary, it takes a lot of time and cost to write the summary, and there is a limitation that the evaluation result may be different depending on the subject of the summarizer. Therefore, in order to overcome these limitations, attempts have been made to measure the quality of summary documents without human intervention. On the other hand, as a representative attempt to overcome these limitations, a method has been recently devised to reduce the size of the full text and to measure the similarity of the reduced full text and the automatic summary. In this method, the more frequent term in the full text appears in the summary, the better the quality of the summary. However, since summarization essentially means minimizing a lot of content while minimizing content omissions, it is unreasonable to say that a "good summary" based on only frequency always means a "good summary" in its essential meaning. In order to overcome the limitations of this previous study of summarization evaluation, this study proposes an automatic quality evaluation for text summarization method based on the essential meaning of summarization. Specifically, the concept of succinctness is defined as an element indicating how few duplicated contents among the sentences of the summary, and completeness is defined as an element that indicating how few of the contents are not included in the summary. In this paper, we propose a method for automatic quality evaluation of text summarization based on the concepts of succinctness and completeness. In order to evaluate the practical applicability of the proposed methodology, 29,671 sentences were extracted from TripAdvisor 's hotel reviews, summarized the reviews by each hotel and presented the results of the experiments conducted on evaluation of the quality of summaries in accordance to the proposed methodology. It also provides a way to integrate the completeness and succinctness in the trade-off relationship into the F-Score, and propose a method to perform the optimal summarization by changing the threshold of the sentence similarity.

Ensemble of Nested Dichotomies for Activity Recognition Using Accelerometer Data on Smartphone (Ensemble of Nested Dichotomies 기법을 이용한 스마트폰 가속도 센서 데이터 기반의 동작 인지)

  • Ha, Eu Tteum;Kim, Jeongmin;Ryu, Kwang Ryel
    • Journal of Intelligence and Information Systems
    • /
    • v.19 no.4
    • /
    • pp.123-132
    • /
    • 2013
  • As the smartphones are equipped with various sensors such as the accelerometer, GPS, gravity sensor, gyros, ambient light sensor, proximity sensor, and so on, there have been many research works on making use of these sensors to create valuable applications. Human activity recognition is one such application that is motivated by various welfare applications such as the support for the elderly, measurement of calorie consumption, analysis of lifestyles, analysis of exercise patterns, and so on. One of the challenges faced when using the smartphone sensors for activity recognition is that the number of sensors used should be minimized to save the battery power. When the number of sensors used are restricted, it is difficult to realize a highly accurate activity recognizer or a classifier because it is hard to distinguish between subtly different activities relying on only limited information. The difficulty gets especially severe when the number of different activity classes to be distinguished is very large. In this paper, we show that a fairly accurate classifier can be built that can distinguish ten different activities by using only a single sensor data, i.e., the smartphone accelerometer data. The approach that we take to dealing with this ten-class problem is to use the ensemble of nested dichotomy (END) method that transforms a multi-class problem into multiple two-class problems. END builds a committee of binary classifiers in a nested fashion using a binary tree. At the root of the binary tree, the set of all the classes are split into two subsets of classes by using a binary classifier. At a child node of the tree, a subset of classes is again split into two smaller subsets by using another binary classifier. Continuing in this way, we can obtain a binary tree where each leaf node contains a single class. This binary tree can be viewed as a nested dichotomy that can make multi-class predictions. Depending on how a set of classes are split into two subsets at each node, the final tree that we obtain can be different. Since there can be some classes that are correlated, a particular tree may perform better than the others. However, we can hardly identify the best tree without deep domain knowledge. The END method copes with this problem by building multiple dichotomy trees randomly during learning, and then combining the predictions made by each tree during classification. The END method is generally known to perform well even when the base learner is unable to model complex decision boundaries As the base classifier at each node of the dichotomy, we have used another ensemble classifier called the random forest. A random forest is built by repeatedly generating a decision tree each time with a different random subset of features using a bootstrap sample. By combining bagging with random feature subset selection, a random forest enjoys the advantage of having more diverse ensemble members than a simple bagging. As an overall result, our ensemble of nested dichotomy can actually be seen as a committee of committees of decision trees that can deal with a multi-class problem with high accuracy. The ten classes of activities that we distinguish in this paper are 'Sitting', 'Standing', 'Walking', 'Running', 'Walking Uphill', 'Walking Downhill', 'Running Uphill', 'Running Downhill', 'Falling', and 'Hobbling'. The features used for classifying these activities include not only the magnitude of acceleration vector at each time point but also the maximum, the minimum, and the standard deviation of vector magnitude within a time window of the last 2 seconds, etc. For experiments to compare the performance of END with those of other methods, the accelerometer data has been collected at every 0.1 second for 2 minutes for each activity from 5 volunteers. Among these 5,900 (=5×(60×22)/0.1) data collected for each activity (the data for the first 2 seconds are trashed because they do not have time window data), 4,700 have been used for training and the rest for testing. Although 'Walking Uphill' is often confused with some other similar activities, END has been found to classify all of the ten activities with a fairly high accuracy of 98.4%. On the other hand, the accuracies achieved by a decision tree, a k-nearest neighbor, and a one-versus-rest support vector machine have been observed as 97.6%, 96.5%, and 97.6%, respectively.

Content-based Recommendation Based on Social Network for Personalized News Services (개인화된 뉴스 서비스를 위한 소셜 네트워크 기반의 콘텐츠 추천기법)

  • Hong, Myung-Duk;Oh, Kyeong-Jin;Ga, Myung-Hyun;Jo, Geun-Sik
    • Journal of Intelligence and Information Systems
    • /
    • v.19 no.3
    • /
    • pp.57-71
    • /
    • 2013
  • Over a billion people in the world generate new news minute by minute. People forecasts some news but most news are from unexpected events such as natural disasters, accidents, crimes. People spend much time to watch a huge amount of news delivered from many media because they want to understand what is happening now, to predict what might happen in the near future, and to share and discuss on the news. People make better daily decisions through watching and obtaining useful information from news they saw. However, it is difficult that people choose news suitable to them and obtain useful information from the news because there are so many news media such as portal sites, broadcasters, and most news articles consist of gossipy news and breaking news. User interest changes over time and many people have no interest in outdated news. From this fact, applying users' recent interest to personalized news service is also required in news service. It means that personalized news service should dynamically manage user profiles. In this paper, a content-based news recommendation system is proposed to provide the personalized news service. For a personalized service, user's personal information is requisitely required. Social network service is used to extract user information for personalization service. The proposed system constructs dynamic user profile based on recent user information of Facebook, which is one of social network services. User information contains personal information, recent articles, and Facebook Page information. Facebook Pages are used for businesses, organizations and brands to share their contents and connect with people. Facebook users can add Facebook Page to specify their interest in the Page. The proposed system uses this Page information to create user profile, and to match user preferences to news topics. However, some Pages are not directly matched to news topic because Page deals with individual objects and do not provide topic information suitable to news. Freebase, which is a large collaborative database of well-known people, places, things, is used to match Page to news topic by using hierarchy information of its objects. By using recent Page information and articles of Facebook users, the proposed systems can own dynamic user profile. The generated user profile is used to measure user preferences on news. To generate news profile, news category predefined by news media is used and keywords of news articles are extracted after analysis of news contents including title, category, and scripts. TF-IDF technique, which reflects how important a word is to a document in a corpus, is used to identify keywords of each news article. For user profile and news profile, same format is used to efficiently measure similarity between user preferences and news. The proposed system calculates all similarity values between user profiles and news profiles. Existing methods of similarity calculation in vector space model do not cover synonym, hypernym and hyponym because they only handle given words in vector space model. The proposed system applies WordNet to similarity calculation to overcome the limitation. Top-N news articles, which have high similarity value for a target user, are recommended to the user. To evaluate the proposed news recommendation system, user profiles are generated using Facebook account with participants consent, and we implement a Web crawler to extract news information from PBS, which is non-profit public broadcasting television network in the United States, and construct news profiles. We compare the performance of the proposed method with that of benchmark algorithms. One is a traditional method based on TF-IDF. Another is 6Sub-Vectors method that divides the points to get keywords into six parts. Experimental results demonstrate that the proposed system provide useful news to users by applying user's social network information and WordNet functions, in terms of prediction error of recommended news.