Experiments were conducted to investigate seed persistence of Chinese milk vetch(CMV) in naturally reseeded rice field in 2007~2009. The seed and pods with seeds were buried in rice field at 0, 5 and 10 cm depths and retrieved from the field at one to three month intervals from July to the following year March and determined change of seed dormancy and viability. In the second experiment, persistence of the CMV seeds in the naturally reseeded rice field at different tillage methods and soil depths were also investigated after rice harvest in autumn. Burial depths and durations affected recovery rate, dormancy and viability of CMV seed. The viability loss was faster and greater in the seed than the pod with seeds and on the soil surface than the 5 or 10 cm burial depths. The recovery rate of CMV seed was decreased starting from one month as seed burial and it was significantly decreased to 52~65% for the seed in September. However, unlike the seed burial, the nearly 100% CMV seeds were recovered for burial as pod with seeds even after four months burial in both 0 and 5 cm depths. However, the recovery rate was sharply declined to below 30% at October in 2007 in both seed and pods with seeds and in the 2008/2009 experiment. the 15~47% of CMV seeds still remained even after October. The CMV had high seed dormancy of 95%, showing only 4~5% germination at the beginning in June but the seed germination increased to 25 to 35% in seed and 55 to 61% in pod with seeds in September due to breakage of hard seed dormancy. The viability loss was faster in the seed than in the pod with seeds regardless of depths of placement in the soil base on decayed seeds. Also the seed placed on the soil surface lost viability faster than the 5~10 burial depths. On the other hand, field observation in the naturally reseeded CMV rice field showed that as many as 917~2,185 CMV seeds
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