As broadcasting and communication are converged recently, communication is jointed to TV. TV viewing has brought about many changes. The IPTV (Internet Protocol Television) provides information service, movie contents, broadcast, etc. through internet with live programs + VOD (Video on demand) jointed. Using communication network, it becomes an issue of new business. In addition, new technical issues have been created by imaging technology for the service, networking technology without video cuts, security technologies to protect copyright, etc. Through this IPTV network, users can watch their desired programs when they want. However, IPTV has difficulties in search approach, menu approach, or finding programs. Menu approach spends a lot of time in approaching programs desired. Search approach can't be found when title, genre, name of actors, etc. are not known. In addition, inserting letters through remote control have problems. However, the bigger problem is that many times users are not usually ware of the services they use. Thus, to resolve difficulties when selecting VOD service in IPTV, a personalized service is recommended, which enhance users' satisfaction and use your time, efficiently. This paper provides appropriate programs which are fit to individuals not to save time in order to solve IPTV's shortcomings through filtering and recommendation-related system. The proposed recommendation system collects TV program information, the user's preferred program genres and detailed genre, channel, watching program, and information on viewing time based on individual records of watching IPTV. To look for these kinds of similarities, similarities can be compared by using ontology for TV programs. The reason to use these is because the distance of program can be measured by the similarity comparison. TV program ontology we are using is one extracted from TV-Anytime metadata which represents semantic nature. Also, ontology expresses the contents and features in figures. Through world net, vocabulary similarity is determined. All the words described on the programs are expanded into upper and lower classes for word similarity decision. The average of described key words was measured. The criterion of distance calculated ties similar programs through K-medoids dividing method. K-medoids dividing method is a dividing way to divide classified groups into ones with similar characteristics. This K-medoids method sets K-unit representative objects. Here, distance from representative object sets temporary distance and colonize it. Through algorithm, when the initial n-unit objects are tried to be divided into K-units. The optimal object must be found through repeated trials after selecting representative object temporarily. Through this course, similar programs must be colonized. Selecting programs through group analysis, weight should be given to the recommendation. The way to provide weight with recommendation is as the follows. When each group recommends programs, similar programs near representative objects will be recommended to users. The formula to calculate the distance is same as measure similar distance. It will be a basic figure which determines the rankings of recommended programs. Weight is used to calculate the number of watching lists. As the more programs are, the higher weight will be loaded. This is defined as cluster weight. Through this, sub-TV programs which are representative of the groups must be selected. The final TV programs ranks must be determined. However, the group-representative TV programs include errors. Therefore, weights must be added to TV program viewing preference. They must determine the finalranks.Based on this, our customers prefer proposed to recommend contents. So, based on the proposed method this paper suggested, experiment was carried out in controlled environment. Through experiment, the superiority of the proposed method is shown, compared to existing ways.
The purpose of this study was to examine pervasive trends in oral cancer in different countries in an effort to discuss what to do to prevent cancer and drop a death rate. The materials of the study were selected from among articles of oral cancer by searching risk factor and epidemiology at a website (www.oraloncology.com). As a result of analyzing the selected literature, it's found that in our country, the percentage of oral cancer in total cancer dropped but the number of oral cancer patients was on the rise every year. In foreign countries, the number of oral cancer patients was on the increase as well, whereas the lethality dropped. In terms of demographic characteristics, the incidence rate of oral cancer was higher among men than women overall. The incidence rate of oral cancer was larger among older people. The major causes of oral cancer were smoking and drinking. To reduce the incidence rate of oral cancer, every possible institutional, administrative and legal measure should be taken to ensure of anti-smoking policies, and publicity of moderation in and abstinence from drinking should be reinforced. The additional causes of oral cancer were demographic characteristics by country and region. The incidence of oral cancer was under the influence of that was affected when the level of personal economy and education was low. Therefore it's important to redress social imbalance within a country and among countries to remove socioeconomic divide. As the oral cancer patients has increased every year, the incidence rate of it should accurately be grasped, and sustained research efforts should be made in consideration of demographic characteristics. Early diagnosis, public oral health education and preventive policies are all required to decrease the incidence rate of oral cancer.
The Muju-Seolcheon area, which is known to be located in the boundary of Ogcheon Belt and Ryeongnam Massif (OB-RM), consists of age unknown or Precambrian metamorphic rocks (MRs) [banded biotite gneiss, metasedimentary rocks (black phyllite, mica schist, crystalline limestone, quartzite), granitic gneiss, hornblendite], Mesozoic sedimentary and igneous rocks. In this paper are researched the structural characteristics of each deformation phase from the geometric and kinematic features and the developing sequence of multi-deformed rock structures of the MRs, and is considered the boundary location of OB-RM with the previous geochemical, radiometric, structure geological data. The geological structure of this area is at least formed through four phases (Dn-1, Dn, Dn+1, Dn+2) of deformation. The Dn-1 is the deformation which took place before the formation of Sn regional foliation and formed Sn-1 foliation folded by Fn fold. The Dn is that which formed the Sn regional foliation. The predominant Sn foliation shows a NE direction which matches the zonal distribution of MRs. A-type or sheath folds, in which the Fn fold axis is parallel to the direction of stretching lineation, are often observed in the crystalline limestone. The Dn+1 deformation, which folded the Sn foliation, took place under compression of NNW~NS direction and formed Fn+1 fold of ENE~EW trend. The Sn foliation is mainly rearranged by Fn+1 folding, and the
Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used