• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.97-117
• /
• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.

• Communications for Statistical Applications and Methods
• /
• v.15 no.4
• /
• pp.563-585
• /
• 2008

This paper presents how to improve the efficiency and accuracy in the pricing and sensitivity evaluation for derivatives, since the need for the evaluation of complicated derivatives is increased. The Monte Carlo(MC) simulation using the quasi random number instead of pseudo random number can improve the elapsed time and accuracy for the valuation of European-type derivatives. However, the quasi MC simulation method has its limit for applying it in the multi-dimensional case such as American-type and path-dependent options due to the increased correlation between dimensions as the dimension of random numbers is increased. In order to complement this problem, we develop a modified method in which correlation values are controlled to be below a pre-specified value. Thus, this method is applicable for the pricing of either derivatives ill which underlying assets or risk factors are several or derivatives having path-dependent or early redemption property. Furthermore, we illustrate that it is important to take an appropriate grid interval for the use of finite difference method(FDM) by applying the FDM to one example of non-symmetrical butterfly spreads.

• Journal of Intelligence and Information Systems
• /
• v.26 no.2
• /
• pp.27-42
• /
• 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 Global Scholars of Marketing Science
• /
• v.13
• /
• pp.1-27
• /
• 2004

This study attempts to investigate the difference between the internet shoppingmall types. The main purpose of this study is to verify the relationships among shoppingmall evaluation criteria, shopping satisfaction, revisit frequency, and repurchase intention across the shoppingmall type. The following results were shown by testing eleven hypotheses using LISREL. First, shoppingmall evaluation criteria such as entertainment, product authentification, economical prices and on-time delivery were significantly related to shopping satisfaction in general merchandise shoppingmall. Second, in specialty shoppingmall, evaluation criteria like informativeness, economical prices and on-time delivery significantly affected shopping satisfaction. Third, as contrasted with the expectation, site design and product assortment did not have significant impact on satisfaction in both internet shoppingmall types. Fourth, shopping satisfaction was significantly related to revist frequency and repeat purchase intention. Finally, some theoretical and managerial implications were discussed.

• Archives of design research
• /
• v.18 no.1 s.59
• /
• pp.205-214
• /
• 2005

A development proposal that maximizes the trend of travel souvenir and articles toward tourism and natural resources through utilization of the Limited Production method was the focus of the present research. First, the flaws of travel souvenir and articles, currently produced in Inje, Kangwon-Do, were identified, as were the regional uniqueness of the area. The comparative advantages of developmental conditions of the above-specified region to various other Si/Kun regions were demonstrated to ascertain the optimal production method of travel souvenir and articles. The results are as follows. Superior supply of,7aw materials necessary for production of wood-worked travel souvenir and articles are abundantly available and the dose proximity of 17 existing workshops self-contain the production capacity to sustain the Limited Production method. Furthermore, the regional governing council conveyed positive attitudes towards the prospect of creating a regional production complex of wood-worked travel souvenir and articles. The results demonstrate that the optimal method of maximizing the trend of travel souvenir and articles toward tourism and natural resources is achieved through systematic collaboration of industry, education, and tourism that promote development, production, and merchandising of Inje's regional travel souvenir and articles.

• Journal of KIISE:Databases
• /
• v.36 no.4
• /
• pp.254-262
• /
• 2009

As the trend of e-business activities develop, customers come into contact with products through on-line shopping sites and lots of customers refer product reviews before the purchasing on-line. However, as the volume of product reviews grow, it takes a great deal of time and effort for customers to read and evaluate voluminous product reviews. Lately, attention is being paid to Opinion Mining(OM) as one of the effective solutions to this problem. In this paper, we propose an efficient method for opinion sentiment classification of product reviews using product specific context information of words occurred in the reviews. We define the context information of words and propose the application of context for sentiment classification and we show the performance of our method through the experiments. Additionally, in case of word corpus construction, we propose the method to construct word corpus automatically using the review texts and review scores in order to prevent traditional manual process. In consequence, we can easily get exact sentiment polarities of opinion words in product reviews.

• Archives of design research
• /
• v.17 no.3
• /
• pp.421-430
• /
• 2004

The aim of this research is to develop communication model and tools that would help in incorporating user experience into information appliance product development. To do so, this research suggested three major modelling techniques, including user-experience model, design strategy model and experience product model. Using these modelling tools, any members in design teams (investigator and designers) will be able to communicate their own ideas and research results each other at any period of the product development process. Each tools have distinct factors that would help identify elements in the user research stages, idea generation stages and product development stages. To verify the efficiency of these models, the case study was conducted with final year students at their studio class. As a result, the benefit of using these tools were identified as 1) these tools accelerate the communication between user researchers, designers and engineers and 2) evaluation process of the product is much more dearer that before. and 3) various user research techniques can be explored.

• The Journal of the Korea Contents Association
• /
• v.22 no.10
• /
• pp.1-10
• /
• 2022

Visually impaired people have very difficulty using retail stores due to the absence of braille information on products and any other support system. In this paper, we propose a basic algorithm for a system that recognizes products in retail stores and explains them as a voice. First, the deep learning model detects hand objects and product objects in the input image. Then, it finds a product object that most overlapping hand object by comparing the coordinate information of each detected object. We determine that this is a product selected by the user, and the system read the nutritional information of the product as Text-To-Speech. As a result of the evaluation, we confirmed a high performance of the learning model. The proposed algorithm can be actively used to build a system that supports the use of retail stores for the visually impaired.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.35 no.1
• /
• pp.83-89
• /
• 1990

A sweet corn hybrid, Golden Cross Bantam 70, was grown at 0, 5, 10, 15 and 20kg/10a of nitrogen (N) under the transparent P. E. film mulch to find the best yield evaluation method. Culm length, ear height, number of tillers increased and silking date was earlier by 1-2 days with increased N level. Leaf area index of main culm at harvest increased with increased N level. Marketable ears were divided into two classes according to the whole sale market price; the frist grade of which husked ear weight over 150g (unhusked ear weight 230g) and the second grade of which husked ear weight between 100 and 150g (unhusked ear weight between 180 and 230g). Average length, thickness, and weight of both grades of marketable ears were not different among the N levels. The proportion of the first grade increased with increased N level. However, total number and weight of marketable ears and gross income per 10a calculated considering weight and number of ears increased with increased N level. There were highly positive correlations between gross income and ear number or ear weight per l0a. The number and weight of marketable ears were underestimated at high N levels compared with gross income. Dry matter yield of stover ranged 740-963kg/10a and increased with increased N level with 20. 8-24.5% dry matter content. Rice black-streaked dwarf virus infection rate was 11.8-15.0%, but it was not related to N level. N concentration in ear was similar but that in stover increased with increased N level. Total N uptake increased but N recovery decreased with increased N level.

• The Journal of Society for e-Business Studies
• /
• v.16 no.4
• /
• pp.125-137
• /
• 2011

Product reviews in online shopping sites can serve as a useful guideline to buying decisions of customers. However, due to the massive amount of such reviews, it is almost impossible for users to read all the product reviews. For this reason, e-commerce sites provide users with useful reviews or statistics of ratings on products that are manually chosen or calculated. Opinion mining or sentiment analysis is a study on automating above process that involves firstly analyzing users' reviews on a product to tell if a review contains positive or negative feedback, and secondly, providing a summarized report of users' opinions. Previous researches focus on either providing polarity of a user's opinion or summarizing user's opinion on a feature of a product that result in relatively low usage of information that a user review contains. Actual user reviews contains not only mere assessment of a product, but also dissatisfaction and flaws of a product that a user experiences. There are increasing needs for effective analysis on such criteria to help users on their decision-making process. This paper proposes a model that stores various types of user reviews in a data warehouse, and analyzes integrated reviews dynamically. Also, we analyze reviews of an online application shopping site with the proposed model.