• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.111-126
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• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.

• Journal of Fisheries and Marine Sciences Education
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• v.7 no.2
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• pp.182-200
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• 1995

Thirty two vessels of the Korean purse seiner had been operated in the Western Pacific Ocean for mainly skipjack tuna, Katsuwonus pelmis LINNAEUS and yellowfin tuna, Thunnus albacares BONNATERRE from January to December in 1991. Among them, fourteen vessels were chosen for this research. During the year their daily operated vessels totalled 4,153 vessels, their total casting net were 2,982 times, in caught 1,798 times, and their total catch was 106,300 M/T. We investigate the distribution of their catch by species, by body size, and by surfance water temperature, and also investigate the distribution of their catch by month and section of the sea, where the sections are separated by 30' of longitude and latitude from the monthly operated sea. We summarize these as follows : 1. The rate of catch by species is 75r/o skipjack tunas, 22.3% yellowfin tunas, and 2.7% bigeye and other tunas. 2. Of the caught skipjack tunas, those of weight 2.0~10kg are most and 68%, those of 1.5~8kg are 11.6%, and those of 3.0~8kg are 9.9%. Of the caught yellowfin tunas, those of weight 5~50kg and 10~50kg are most and 23.1%, and 28.3% respectively, those of 20~50kg are 15.8%, weight 30~50kg are 12.5%, and weight 2~50kg are 9.7%. 3. On the distribution of catch by surface water temperature, 49% of catch are taken between $29.0^{\circ}C$ and $29.4^{\circ}C$, 37% are taken between $29.5^{\circ}C$ and $29.9^{\circ}C$, and about 6% are taken between $28.5^{\circ}C$ and $28.9^{\circ}C$, but very little, only about 1% are taken below $28.4^{\circ}C$ and above $30.5^{\circ}C$. 4. On the distribution of catch by month and section of sea, skipjack tunas are most caught 10,618M/T in August and 10,412M/T in September in the section of Lat. $3^{\circ}{\sim}6^{\circ}S$ and Long. $174^{\circ}E{\sim}176^{\circ}W$, caught much 8,825M/I' in June and 8,057M/T in January in section of Lat. $1^{\circ}S{\sim}3^{\circ}N$ and Long. $142^{\circ}{\sim}151^{\circ}$E, but caught very little in May, November and December in the costal area of New Guinea. Yellowfin tunas are mostly caught 4,070M/T in June in the section of Lat. $0^{\circ}{\sim}4^{\circ}$N and Long. $142^{\circ}{\sim}151^{\circ}$E, and caught much over 2,000M/T in February~April and October~December in the section of coastal area and near islands, but caught very little in distant water area.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.27-42
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• 2020

Traditional companies with offline stores were unable to secure large display space due to the problems of cost. This limitation inevitably allowed limited kinds of products to be displayed on the shelves, which resulted in consumers being deprived of the opportunity to experience various items. Taking advantage of the virtual space called the Internet, online shopping goes beyond the limits of limitations in physical space of offline shopping and is now able to display numerous products on web pages that can satisfy consumers with a variety of needs. Paradoxically, however, this can also cause consumers to experience the difficulty of comparing and evaluating too many alternatives in their purchase decision-making process. As an effort to address this side effect, various kinds of consumer's purchase decision support systems have been studied, such as keyword-based item search service and recommender systems. These systems can reduce search time for items, prevent consumer from leaving while browsing, and contribute to the seller's increased sales. Among those systems, recommender systems based on association rule mining techniques can effectively detect interrelated products from transaction data such as orders. The association between products obtained by statistical analysis provides clues to predicting how interested consumers will be in another product. However, since its algorithm is based on the number of transactions, products not sold enough so far in the early days of launch may not be included in the list of recommendations even though they are highly likely to be sold. Such missing items may not have sufficient opportunities to be exposed to consumers to record sufficient sales, and then fall into a vicious cycle of a vicious cycle of declining sales and omission in the recommendation list. This situation is an inevitable outcome in situations in which recommendations are made based on past transaction histories, rather than on determining potential future sales possibilities. This study started with the idea that reflecting the means by which this potential possibility can be identified indirectly would help to select highly recommended products. In the light of the fact that the attributes of a product affect the consumer's purchasing decisions, this study was conducted to reflect them in the recommender systems. In other words, consumers who visit a product page have shown interest in the attributes of the product and would be also interested in other products with the same attributes. On such assumption, based on these attributes, the recommender system can select recommended products that can show a higher acceptance rate. Given that a category is one of the main attributes of a product, it can be a good indicator of not only direct associations between two items but also potential associations that have yet to be revealed. Based on this idea, the study devised a recommender system that reflects not only associations between products but also categories. Through regression analysis, two kinds of associations were combined to form a model that could predict the hit rate of recommendation. To evaluate the performance of the proposed model, another regression model was also developed based only on associations between products. Comparative experiments were designed to be similar to the environment in which products are actually recommended in online shopping malls. First, the association rules for all possible combinations of antecedent and consequent items were generated from the order data. Then, hit rates for each of the associated rules were predicted from the support and confidence that are calculated by each of the models. The comparative experiments using order data collected from an online shopping mall show that the recommendation accuracy can be improved by further reflecting not only the association between products but also categories in the recommendation of related products. The proposed model showed a 2 to 3 percent improvement in hit rates compared to the existing model. From a practical point of view, it is expected to have a positive effect on improving consumers' purchasing satisfaction and increasing sellers' sales.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Journal of Fisheries and Marine Sciences Education
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• v.4 no.1
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• pp.30-46
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• 1992

Korea's first exploratory tuna fishing was done with a used longliner in 1957. Then the commercial fishing has been made steady headway since the 1960's and grown up to one of major tuna fishing countries in 1970's. The tuna fishing aimed primarily at acquiring foreign currency, then tuna was exported directly from the overseas fishing base. Tuna, however, has been gradually favored by Koreans as high-proteined foods according to the growth of GNP since the 1970's. In 1980, the canned tuna began to be produced and sold at home. And so the demand of raw tuna for cannaries has steeply increased not only for home but also for abroad, and stimulated the development of tuna purse seine fishery. The author carried out a study on the development of tuna purse seine fishery in Korea and countries concerned-the United States and Japan-because it is recognized to be significant for the further development of this fishery. Just as purse seining was originated in the United States, so tuna purse seining was also pioneered by Californian fishermen in the west coastal waters of the United States (Eastern Pacific Ocean). They started to produce the canned tuna in the early 1900's, and the demand for raw tuna began to be increased rapidly. In those days, tuna was mostly caught by pole-and-line, but the catch amount was far away from the demand. To satisfy this demand, they began to try out fishing tuna by the use of purse seine which had been born in the eastern waters in the 1820's and applied to catch white fishes in the western waters of the United States in those days. Even though their trial was technically successful through severe trial and error, a new problem was raised on the management of tuna resource and the preservation of porpoise which was occassionally caught with tuna. Then the Inter-American Tropical Tuna Commission (IATTC) was established by countries neighboring to the United States in 1950 and they set up the Commission's Yellowfin Regulatory Area (CYRA) and regulated the annual quota for yellowfin. Then, American owners tried to send their seiners to the Western African waters to expand the fishing ground in 1967 and to the Centeral-Western Pacfic in 1974, and the fishing ground was widely expanded. The number of the United States' purse seiners amounted to about 150 in 1980, but the enthusiasm was gradually cooled thereafter and the number of seiner was decreased to 67 in 1986. The landing of tuna by purse seiners in the United States after 1980 maintains 200 thousands M/T or so with a little increase despite the decreasing of domestic seiners. This shows that the landing by foreign seiners are increasing, compared with the landing by domestic seiners are decreasing. In Japan, even though purse seining was introduced in 1880, they had fished tuna by longline and pole-and -line until the tuna purse seining was introduced from the United States again. In the 1960's, Japanese tuna seiners made the exploratory fishing in the South-western Pacific and West African waters with a limited success. In 1971, the government-funded research center "JARMRAC" conducted the exploratory fishing which extended to the Central American waters, the Asia-Pacific Region and the South-western Pacific. It had also much difficulties, till they improved the fishing gear adaptable to the new fishing condition in the South-western Pacific. Japanese government has begun to licence 32 single seiners and 7 group seiners since 1980 and their standard has lasted up to now. The catch in the Pacific Islands Region amounted to 160 thousands M/T in 1986. Korea's tuna purse seine fishery was originated in 1971 by Jedong Industrial Co., Ltd. with three used tuna purse seiners purchased from the United States, and they began to fish in the Eastern Pacific, but failed owing to the superannuation of vessel and the infancy of fishing technique. The second challenge was done by Dongwon Industrial Co., Ltd. in 1979, with one used seiner purchased from the United States, and started to fish in the Eastern Pacific. Even though the first trial was almost unsuccessful but they could obtain the noticeable success by removing the vessel to the South-western Pacific in 1980. This success stimulated the Korean entherprisers to take part in this fishery, and the number of Korean tuna purse seiners has been increased rapidly in accordance with the increased demand for raw tuna. The number of vessels actually at work amounted to 36 in 1990 and they operate in the South-western Pacific. The annual catch of tuna by purse seiners amounted to 170 thousands M/T in 1990 and ranked to one of the major tuna purse seining countries in the world.