Recommender system using BERT sentiment analysis (BERT 기반 감성분석을 이용한 추천시스템)
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- Journal of Intelligence and Information Systems
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- v.27 no.2
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- pp.1-15
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- 2021
If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.
There has been a growing interest in IPOs (Initial Public Offerings) due to the profitable returns that IPO stocks can offer to investors. However, IPOs can be speculative investments that may involve substantial risk as well because shares tend to be volatile, and the supply of IPO shares is often highly limited. Therefore, it is crucially important that IPO investors are well informed of the issuing firms and the market before deciding whether to invest or not. Unlike institutional investors, individual investors are at a disadvantage since there are few opportunities for individuals to obtain information on the IPOs. In this regard, the purpose of this study is to provide individual investors with the information they may consider when making an IPO investment decision. This study presents a model that uses machine learning and text analysis to predict whether an IPO stock price would move up or down after the first 5 trading days. Our sample includes 691 Korean IPOs from June 2009 to December 2020. The input variables for the prediction are three tone variables created from IPO prospectuses and quantitative variables that are either firm-specific, issue-specific, or market-specific. The three prospectus tone variables indicate the percentage of positive, neutral, and negative sentences in a prospectus, respectively. We considered only the sentences in the Risk Factors section of a prospectus for the tone analysis in this study. All sentences were classified into 'positive', 'neutral', and 'negative' via text analysis using TF-IDF (Term Frequency - Inverse Document Frequency). Measuring the tone of each sentence was conducted by machine learning instead of a lexicon-based approach due to the lack of sentiment dictionaries suitable for Korean text analysis in the context of finance. For this reason, the training set was created by randomly selecting 10% of the sentences from each prospectus, and the sentence classification task on the training set was performed after reading each sentence in person. Then, based on the training set, a Support Vector Machine model was utilized to predict the tone of sentences in the test set. Finally, the machine learning model calculated the percentages of positive, neutral, and negative sentences in each prospectus. To predict the price movement of an IPO stock, four different machine learning techniques were applied: Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network. According to the results, models that use quantitative variables using technical analysis and prospectus tone variables together show higher accuracy than models that use only quantitative variables. More specifically, the prediction accuracy was improved by 1.45% points in the Random Forest model, 4.34% points in the Artificial Neural Network model, and 5.07% points in the Support Vector Machine model. After testing the performance of these machine learning techniques, the Artificial Neural Network model using both quantitative variables and prospectus tone variables was the model with the highest prediction accuracy rate, which was 61.59%. The results indicate that the tone of a prospectus is a significant factor in predicting the price movement of an IPO stock. In addition, the McNemar test was used to verify the statistically significant difference between the models. The model using only quantitative variables and the model using both the quantitative variables and the prospectus tone variables were compared, and it was confirmed that the predictive performance improved significantly at a 1% significance level.
While the role of emotional antecedents of effective selling behavior would be important, the issue has not been fully addressed in the sales literature. To fill this gap, we conceptualize and empirically examine the relationships among salesperson's emotional regulation processes such as emotional intelligence (EI) and emotional labor (EL), effective selling behavior, and sales performance on the basis of educational, occupational, social psychology literature and marketing literature (e.g., Henning-Thurau, Groth, Paul, and Gremler 2006; Kidwell et al. 2011; Liu et al. 2008; Mayer, Salovey, and Caruso 2008). First, salesperson's EI is defined as his or her capability that enables correct perceptions about emotional situations in sales interactions. The EI is expected to work as psychological resources for different types of EL (i.e., deep acting and surface acting) to be performed by salesperson as emotional expression strategies (e.g., Lie et al. 2008). It is, then, expected that the features of EL selected by the salesperson would lead to different levels of adaptive selling behavior (ASB) and thereby sales performance (Monaghan 2006). Further, given that salesperson's customer orientation (CO) is found to be an important correlate of ASB (Franke and Park 2006), it is expected that CO would moderate the relationship between EL and ASB (Rozell, Pettijohn, and Parker 2004). Hence, this research attempts to shed additional light on emotionally-driven (EL) as well as cognitively-driven (CO) antecedents of ASB (Frank and Park 2006). The findings of the survey research, done with 336 salespersons in insurance and financial companies, are summarized as follows. First, salespersons with a high level of EI are found to use both deep acting (regulating the emotions themselves) and surface acting (controlling only emotional expressions) in a versatile way, when implementing EL. Second, the more the salesperson performs deep acting, the more he or she shows ASB. It is, then, important for salespersons to use deep acting more frequently in the EL process in order to enhance the quality of interacting with customers through ASB. On the other hand, the salesperson's surface acting did not have a significant relationship with ASB. Moreover, CO was found to moderate the relationship between the salesperson's deep acting and ASB. That is, the context of high CO culture and individual salesperson's deep acting would synergistically make the selling efforts adaptive to customer preferences. Conceptualizing and empirically verifying the antecedent roles of important emotional constructs such as EI and EL in salesperson's effective selling behavior (ASB) and sales performance is a major theoretical contribution in the sales literature. Managerially, this research provides a deeper understanding on the nature of tasks performed by salespersons in service industries and a few guidelines for managing the sales force. First, sales organizations had better consciously assess EI capacity in the selection and nurturing processes of salespersons, given that EI can efficiently drive EL and the resulting effective selling behavior and performance. Further, the concept of EL could provide a framework to understand the salespersons' emotional experiences in depth. Especially, sales organizations may well think over how to develop deep acting capabilities of their sales representatives. In this direction, the training on deep acting strategies would be an essential task for improving effective selling behavior and performance of salespersons. This kind of training had better incorporate the perspectives of customers such that many customers can actually discern whether salespersons are doing either surface acting or deep acting. Finally, based on the synergistic effects of deep acting and CO culture, how to build and sustain CO is always an ever-important task in sales organizations. While the prior sales literature has emphasized the process and structure of highly customer-oriented sales organization, our research not only corroborates the important aspects of customer-oriented sales organization, but also adds the important dimension of competent sales representatives who can resonate with customers by deep acting for sales excellence.
Research on purchase intention had showed remarkable progress in contributing to the theory of consumer behavior. Despite the popularity of academic discussions on various issues influencing purchase intention, the mediating role of brand recall and brand attitude on purchase intention, in particular advergames, has not yet been well explored, especially in developing economies such as Malaysia. Furthermore, the influence of self-brand congruity, entertainment and brand placement acceptance as antecedents to brand recall and brand attitude are expected to add value to the brand recall, brand attitude and purchase intention relationships. This paper aims to investigate the impact of the mediating role of brand recall and brand attitude on purchase intention in advergames among Malaysian gamers. The antecedents towards brand recall and brand attitude are also examined to measure their influence on purchase intention. A total of 350 Malaysian gamers were interviewed through online survey in this study. The results showed that brand recall and brand attitude mediate the relationship between self-brand congruity, entertainment and brand placement acceptance with purchase intention. Also, entertainment and brand placement acceptance were found to have a significant relationship with brand recall. As expected, brand recall and brand attitude showed a positive relationship with consumer purchase intention in advergames. It was found that self-brand congruity has a significant influence on brand attitude and purchase intention. When users see the brand which matches with them, they tend to act positively toward the brand exposed in the game. This is consistent with Escalas and Bettman (2005) who suggested that the greater the congruity, the more positive the consumer's attitude toward the brand in question. This leads to game usage and purchase (Davis and Lang, 2013). In the advergaming context, the entertainment value in advergames is very important to determine the level of enjoyment and pleasure experienced by gamers during game-playing. Therefore, the more entertaining the ads, the more it will be remembered and the greater the positive behavior of the consumer towards the advergames - this ultimately stimulates their intention to purchase the brand. This study shows the effect of brand placement on brand recall and brand attitude and also purchase intention. Brand placements might not work in games due to the interactivity involved in game-playing as people could be distracted from noticing the brand placements (Yang et al., 2006). However, the significant influence of brand placements found in this study may provide major promise for advertisers. Game players may or may not explicitly remember the brands they see in the games, but these placements may influence their brand recall and brand attitude and could therefore influence later decisions (Yang et al., 2006). In this study, it was found that self-brand congruity was not significantly related with brand recall. The reason for this could be attributed to the fact that this study examined gamers who are highly involved in the interactive medium of games which force them to focus on game play rather than advertisements; the level of recognition to remember the brand exposed in the game is low and contributes to the "mismatch" between the gamers and the said brand. The present study contributes to the existing literature of the antecedents of brand recall and brand attitude in advergames. This study contributes to the role of brand recall and brand attitude as mediators in purchase behavior theory. Academically, the relationship between brand recall and brand attitude is well known in advergaming research, but their impact as important mediators on purchase intention add new understanding in the interactive communication literature. Their mediating role may provide new insights on how they facilitate the effects of self-brand congruity, entertainment and brand placement on purchase intention. Besides that, the studies on the influence of self-brand congruity on brand recall and brand attitude and also consumer intention to purchase had not well-investigated in advergames. This study contributes to fill those gaps in advergames literature. For practitioners, this study could suggest the use of illustrative or demonstrative placements of new products to help customers remember new brands, and the use of associative placements for existing products to increase consumers' purchase intention (Ho et al., 2011). To advertisers, this study may provide useful information to improve their current advertising strategies in games, for instance, by considering game players' congruity, entertainment value and brand placement factors.
At the initial stage of Internet advertising, banner advertising came into fashion. As the Internet developed into a central part of daily lives and the competition in the on-line advertising market was getting fierce, there was not enough space for banner advertising, which rushed to portal sites only. All these factors was responsible for an upsurge in advertising prices. Consequently, the high-cost and low-efficiency problems with banner advertising were raised, which led to an emergence of keyword advertising as a new type of Internet advertising to replace its predecessor. In the beginning of 2000s, when Internet advertising came to be activated, display advertisement including banner advertising dominated the Net. However, display advertising showed signs of gradual decline, and registered minus growth in the year 2009, whereas keyword advertising showed rapid growth and started to outdo display advertising as of the year 2005. Keyword advertising refers to the advertising technique that exposes relevant advertisements on the top of research sites when one searches for a keyword. Instead of exposing advertisements to unspecified individuals like banner advertising, keyword advertising, or targeted advertising technique, shows advertisements only when customers search for a desired keyword so that only highly prospective customers are given a chance to see them. In this context, it is also referred to as search advertising. It is regarded as more aggressive advertising with a high hit rate than previous advertising in that, instead of the seller discovering customers and running an advertisement for them like TV, radios or banner advertising, it exposes advertisements to visiting customers. Keyword advertising makes it possible for a company to seek publicity on line simply by making use of a single word and to achieve a maximum of efficiency at a minimum cost. The strong point of keyword advertising is that customers are allowed to directly contact the products in question through its more efficient advertising when compared to the advertisements of mass media such as TV and radio, etc. The weak point of keyword advertising is that a company should have its advertisement registered on each and every portal site and finds it hard to exercise substantial supervision over its advertisement, there being a possibility of its advertising expenses exceeding its profits. Keyword advertising severs as the most appropriate methods of advertising for the sales and publicity of small and medium enterprises which are in need of a maximum of advertising effect at a low advertising cost. At present, keyword advertising is divided into CPC advertising and CPM advertising. The former is known as the most efficient technique, which is also referred to as advertising based on the meter rate system; A company is supposed to pay for the number of clicks on a searched keyword which users have searched. This is representatively adopted by Overture, Google's Adwords, Naver's Clickchoice, and Daum's Clicks, etc. CPM advertising is dependent upon the flat rate payment system, making a company pay for its advertisement on the basis of the number of exposure, not on the basis of the number of clicks. This method fixes a price for advertisement on the basis of 1,000-time exposure, and is mainly adopted by Naver's Timechoice, Daum's Speciallink, and Nate's Speedup, etc, At present, the CPC method is most frequently adopted. The weak point of the CPC method is that advertising cost can rise through constant clicks from the same IP. If a company makes good use of strategies for maximizing the strong points of keyword advertising and complementing its weak points, it is highly likely to turn its visitors into prospective customers. Accordingly, an advertiser should make an analysis of customers' behavior and approach them in a variety of ways, trying hard to find out what they want. With this in mind, her or she has to put multiple keywords into use when running for ads. When he or she first runs an ad, he or she should first give priority to which keyword to select. The advertiser should consider how many individuals using a search engine will click the keyword in question and how much money he or she has to pay for the advertisement. As the popular keywords that the users of search engines are frequently using are expensive in terms of a unit cost per click, the advertisers without much money for advertising at the initial phrase should pay attention to detailed keywords suitable to their budget. Detailed keywords are also referred to as peripheral keywords or extension keywords, which can be called a combination of major keywords. Most keywords are in the form of texts. The biggest strong point of text-based advertising is that it looks like search results, causing little antipathy to it. But it fails to attract much attention because of the fact that most keyword advertising is in the form of texts. Image-embedded advertising is easy to notice due to images, but it is exposed on the lower part of a web page and regarded as an advertisement, which leads to a low click through rate. However, its strong point is that its prices are lower than those of text-based advertising. If a company owns a logo or a product that is easy enough for people to recognize, the company is well advised to make good use of image-embedded advertising so as to attract Internet users' attention. Advertisers should make an analysis of their logos and examine customers' responses based on the events of sites in question and the composition of products as a vehicle for monitoring their behavior in detail. Besides, keyword advertising allows them to analyze the advertising effects of exposed keywords through the analysis of logos. The logo analysis refers to a close analysis of the current situation of a site by making an analysis of information about visitors on the basis of the analysis of the number of visitors and page view, and that of cookie values. It is in the log files generated through each Web server that a user's IP, used pages, the time when he or she uses it, and cookie values are stored. The log files contain a huge amount of data. As it is almost impossible to make a direct analysis of these log files, one is supposed to make an analysis of them by using solutions for a log analysis. The generic information that can be extracted from tools for each logo analysis includes the number of viewing the total pages, the number of average page view per day, the number of basic page view, the number of page view per visit, the total number of hits, the number of average hits per day, the number of hits per visit, the number of visits, the number of average visits per day, the net number of visitors, average visitors per day, one-time visitors, visitors who have come more than twice, and average using hours, etc. These sites are deemed to be useful for utilizing data for the analysis of the situation and current status of rival companies as well as benchmarking. As keyword advertising exposes advertisements exclusively on search-result pages, competition among advertisers attempting to preoccupy popular keywords is very fierce. Some portal sites keep on giving priority to the existing advertisers, whereas others provide chances to purchase keywords in question to all the advertisers after the advertising contract is over. If an advertiser tries to rely on keywords sensitive to seasons and timeliness in case of sites providing priority to the established advertisers, he or she may as well make a purchase of a vacant place for advertising lest he or she should miss appropriate timing for advertising. However, Naver doesn't provide priority to the existing advertisers as far as all the keyword advertisements are concerned. In this case, one can preoccupy keywords if he or she enters into a contract after confirming the contract period for advertising. This study is designed to take a look at marketing for keyword advertising and to present effective strategies for keyword advertising marketing. At present, the Korean CPC advertising market is virtually monopolized by Overture. Its strong points are that Overture is based on the CPC charging model and that advertisements are registered on the top of the most representative portal sites in Korea. These advantages serve as the most appropriate medium for small and medium enterprises to use. However, the CPC method of Overture has its weak points, too. That is, the CPC method is not the only perfect advertising model among the search advertisements in the on-line market. So it is absolutely necessary that small and medium enterprises including independent shopping malls should complement the weaknesses of the CPC method and make good use of strategies for maximizing its strengths so as to increase their sales and to create a point of contact with customers.
The scarcity is pervasive aspect of human life and is a fundamental precondition of economic behavior of consumers. Also, the effect of scarcity message is a power social influence principle used by marketers to increase the subjective desirability of products. Because valuable objects are often scare, consumers tend to infer the scarce objects are valuable. Marketers often do base promotional appeals on the principle of scarcity to increase the subjective desirability their products among consumers. Specially, advertisers and retailers often promote their products using restrictions. These restriction act to constraint consumers' ability th take advantage of the promotion and can assume several forms. For example, some promotions are advertised as limited time offers, while others limit the quantity that can be bought at the deal price by employing the statements such as 'limit one per consumer,' 'limit 5 per customer,' 'limited products for special commemoration celebration,' Some retailers use statements extensively. A recent weekly flyer by a prominent retailer limited purchase quantities on 50% of the specials advertised on front page. When consumers saw these phrase, they often infer value from the product that has limited availability or is promoted as being scarce. But, the past researchers explored a direct relationship between the purchase quantity and time limit on deal purchase intention. They also don't explored that all restriction message are not created equal. Namely, we thought that different restrictions signal deal value in different ways or different mechanism. Consumers appear to perceive that time limits are used to attract consumers to the brand, while quantity limits are necessary to reduce stockpiling. This suggests other possible differences across restrictions. For example, quantity limits could imply product quality (i.e., this product at this price is so good that purchases must be limited). In contrast, purchase preconditions force the consumer to spend a certain amount to qualify for the deal, which suggests that inferences about the absolute quality of the promoted item would decline from purchase limits (highest quality) to time limits to purchase preconditions (lowest quality). This might be expected to be particularly true for unfamiliar brands. However, a critical but elusive issue in scarcity message research is the impacts of a inferred motives on the promoted scarcity message. The past researchers not explored possibility of inferred motives on the scarcity message context. Despite various type to the quantity limits message, they didn't separated scarcity message among the quantity limits. Therefore, we apply a stricter definition of scarcity message(i.e. quantity limits) and consider scarcity message type(general scarcity message vs. special scarcity message), scarcity depth(high vs. low). The purpose of this study is to examine the effect of the scarcity message on the consumer's purchase intension. Specifically, we investigate the effect of general versus special scarcity messages on the consumer's purchase intention using the level of the scarcity depth as moderators. In other words, we postulates that the scarcity message type and scarcity depth play an essential moderating role in the relationship between the inferred motives and purchase intention. In other worlds, different from the past studies, we examine the interplay between the perceived motives and scarcity type, and between the perceived motives and scarcity depth. Both of these constructs have been examined in isolation, but a key question is whether they interact to produce an effect in reaction to the scarcity message type or scarcity depth increase. The perceived motive Inference behind the scarcity message will have important impact on consumers' reactions to the degree of scarcity depth increase. In relation ti this general question, we investigate the following specific issues. First, does consumers' inferred motives weaken the positive relationship between the scarcity depth decrease and the consumers' purchase intention, and if so, how much does it attenuate this relationship? Second, we examine the interplay between the scarcity message type and the consumers' purchase intention in the context of the scarcity depth decrease. Third, we study whether scarcity message type and scarcity depth directly affect the consumers' purchase intention. For the answer of these questions, this research is composed of 2(intention inference: existence vs. nonexistence)
An Adaptive Clustering-based Collaborative Filtering Technique was proposed to solve the fundamental problems of collaborative filtering, such as cold-start problems, scalability problems and data sparsity problems. Previous collaborative filtering techniques were carried out according to the recommendations based on the predicted preference of the user to a particular item using a similar item subset and a similar user subset composed based on the preference of users to items. For this reason, if the density of the user preference matrix is low, the reliability of the recommendation system will decrease rapidly. Therefore, the difficulty of creating a similar item subset and similar user subset will be increased. In addition, as the scale of service increases, the time needed to create a similar item subset and similar user subset increases geometrically, and the response time of the recommendation system is then increased. To solve these problems, this paper suggests a collaborative filtering technique that adapts a condition actively to the model and adopts the concepts of a context-based filtering technique. This technique consists of four major methodologies. First, items are made, the users are clustered according their feature vectors, and an inter-cluster preference between each item cluster and user cluster is then assumed. According to this method, the run-time for creating a similar item subset or user subset can be economized, the reliability of a recommendation system can be made higher than that using only the user preference information for creating a similar item subset or similar user subset, and the cold start problem can be partially solved. Second, recommendations are made using the prior composed item and user clusters and inter-cluster preference between each item cluster and user cluster. In this phase, a list of items is made for users by examining the item clusters in the order of the size of the inter-cluster preference of the user cluster, in which the user belongs, and selecting and ranking the items according to the predicted or recorded user preference information. Using this method, the creation of a recommendation model phase bears the highest load of the recommendation system, and it minimizes the load of the recommendation system in run-time. Therefore, the scalability problem and large scale recommendation system can be performed with collaborative filtering, which is highly reliable. Third, the missing user preference information is predicted using the item and user clusters. Using this method, the problem caused by the low density of the user preference matrix can be mitigated. Existing studies on this used an item-based prediction or user-based prediction. In this paper, Hao Ji's idea, which uses both an item-based prediction and user-based prediction, was improved. The reliability of the recommendation service can be improved by combining the predictive values of both techniques by applying the condition of the recommendation model. By predicting the user preference based on the item or user clusters, the time required to predict the user preference can be reduced, and missing user preference in run-time can be predicted. Fourth, the item and user feature vector can be made to learn the following input of the user feedback. This phase applied normalized user feedback to the item and user feature vector. This method can mitigate the problems caused by the use of the concepts of context-based filtering, such as the item and user feature vector based on the user profile and item properties. The problems with using the item and user feature vector are due to the limitation of quantifying the qualitative features of the items and users. Therefore, the elements of the user and item feature vectors are made to match one to one, and if user feedback to a particular item is obtained, it will be applied to the feature vector using the opposite one. Verification of this method was accomplished by comparing the performance with existing hybrid filtering techniques. Two methods were used for verification: MAE(Mean Absolute Error) and response time. Using MAE, this technique was confirmed to improve the reliability of the recommendation system. Using the response time, this technique was found to be suitable for a large scaled recommendation system. This paper suggested an Adaptive Clustering-based Collaborative Filtering Technique with high reliability and low time complexity, but it had some limitations. This technique focused on reducing the time complexity. Hence, an improvement in reliability was not expected. The next topic will be to improve this technique by rule-based filtering.
In recent years, mobile phone has experienced an extremely fast evolution. It is equipped with high-quality color displays, high resolution cameras, and real-time accelerated 3D graphics. In addition, some other features are includes GPS sensor and Digital Compass, etc. This evolution advent significantly helps the application developers to use the power of smart-phones, to create a rich environment that offers a wide range of services and exciting possibilities. To date mobile AR in outdoor research there are many popular location-based AR services, such Layar and Wikitude. These systems have big limitation the AR contents hardly overlaid on the real target. Another research is context-based AR services using image recognition and tracking. The AR contents are precisely overlaid on the real target. But the real-time performance is restricted by the retrieval time and hardly implement in large scale area. In our work, we exploit to combine advantages of location-based AR with context-based AR. The system can easily find out surrounding landmarks first and then do the recognition and tracking with them. The proposed system mainly consists of two major parts-landmark browsing module and annotation module. In landmark browsing module, user can view an augmented virtual information (information media), such as text, picture and video on their smart-phone viewfinder, when they pointing out their smart-phone to a certain building or landmark. For this, landmark recognition technique is applied in this work. SURF point-based features are used in the matching process due to their robustness. To ensure the image retrieval and matching processes is fast enough for real time tracking, we exploit the contextual device (GPS and digital compass) information. This is necessary to select the nearest and pointed orientation landmarks from the database. The queried image is only matched with this selected data. Therefore, the speed for matching will be significantly increased. Secondly is the annotation module. Instead of viewing only the augmented information media, user can create virtual annotation based on linked data. Having to know a full knowledge about the landmark, are not necessary required. They can simply look for the appropriate topic by searching it with a keyword in linked data. With this, it helps the system to find out target URI in order to generate correct AR contents. On the other hand, in order to recognize target landmarks, images of selected building or landmark are captured from different angle and distance. This procedure looks like a similar processing of building a connection between the real building and the virtual information existed in the Linked Open Data. In our experiments, search range in the database is reduced by clustering images into groups according to their coordinates. A Grid-base clustering method and user location information are used to restrict the retrieval range. Comparing the existed research using cluster and GPS information the retrieval time is around 70~80ms. Experiment results show our approach the retrieval time reduces to around 18~20ms in average. Therefore the totally processing time is reduced from 490~540ms to 438~480ms. The performance improvement will be more obvious when the database growing. It demonstrates the proposed system is efficient and robust in many cases.
As so many marketers get to use diverse sales promotion methods, manufacturer and retailer in a channel often use them too. In this context, diverse issues on sales promotion management arise. One of them is the issue of unplanned buying. Consumers' unplanned buying is clearly better off for the retailer but not for manufacturer. This asymmetric influence of unplanned buying should be dealt with prudently because of its possibility of provocation of channel conflict. However, there have been scarce studies on the sales promotion management strategy considering the unplanned buying and its asymmetric effect on retailer and manufacturer. In this paper, we try to find a better way for a manufacturer in a channel to promote performance through the retailer's sales promotion efforts when there is potential of unplanned buying effect. We investigate via game-theoretic modeling what is the optimal cost sharing level between the manufacturer and retailer when there is unplanned buying effect. We investigated following issues about the topic as follows: (1) What structure of cost sharing mechanism should the manufacturer and retailer in a channel choose when unplanned buying effect is strong (or weak)? (2) How much payoff could the manufacturer and retailer in a channel get when unplanned buying effect is strong (or weak)? We focus on the impact of unplanned buying effect on the optimal cost sharing mechanism for sales promotions between a manufacturer and a retailer in a same channel. So we consider two players in the game, a manufacturer and a retailer who are interacting in a same distribution channel. The model is of complete information game type. In the model, the manufacturer is the Stackelberg leader and the retailer is the follower. Variables in the model are as following table. Manufacturer's objective function in the basic game is as follows:
The purpose of the study was to introduce an integrated approach of new Casuistry and specified principlism in resolving ethical problems and studying nursing ethics. In studying clinical ethics and nursing ethics, there is no systematic research method. While nurses often experience ethical dilemmas in practice, much of previous research on nursing ethics has focused merely on describing the existing problems. In addition, ethists presented theoretical analysis and critics rather than providing the specific problems solving strategies. There is a need in clinical situations for an integrated method which can provide the objective description for existing problem situations as well as specific problem solving methods. We inherit two distinct ways of discussing ethical issues. One of these frames these issues in terms of principles, rules, and other general ideas; the other focuses on the specific features of particular kinds of moral cases. In the first way general ethical rules relate to specific moral cases in a theoretical manner, with universal rules serving as "axioms" from which particular moral judgments are deduced as theorems. In the seconds, this relation is frankly practical. with general moral rules serving as "maxims", which can be fully understood only in terms of the paradigmatic cases that define their meaning and force. Theoretical arguments are structured in ways that free them from any dependence on the circumstances of their presentation and ensure them a validity of a kind that is not affected by the practical context of use. In formal arguments particular conclusions are deduced from("entailed by") the initial axioms or universal principles that are the apex of the argument. So the truth or certainty that attaches to those axioms flows downward to the specific instances to be "proved". In the language of formal logic, the axioms are major premises, the facts that specify the present instance are minor premises, and the conclusion to be "proved" is deduced (follows necessarily) from the initial presises. Practical arguments, by contrast, involve a wider range of factors than formal deductions and are read with an eye to their occasion of use. Instead of aiming at strict entailments, they draw on the outcomes of previous experience, carrying over the procedures used to resolve earlier problems and reapply them in new problmatic situations. Practical arguments depend for their power on how closely the present circumstances resemble those of the earlier precedent cases for which this particular type of argument was originally devised. So. in practical arguments, the truths and certitudes established in the precedent cases pass sideways, so as to provide "resolutions" of later problems. In the language of rational analysis, the facts of the present case define the gounds on which any resolution must be based; the general considerations that carried wight in similar situations provide warrants that help settle future cases. So the resolution of any problem holds good presumptively; its strengh depends on the similarities between the present case and the prededents; and its soundness can be challenged (or rebutted) in situations that are recognized ans exceptional. Jonsen & Toulmin (1988), and Jonsen (1991) introduce New Casuistry as a practical method. The oxford English Dictionary defines casuistry quite accurately as "that part of ethics which resolves cases of conscience, applying the general rules of religion and morality to particular instances in which circumstances alter cases or in which there appears to be a conflict of duties." They modified the casuistry of the medieval ages to use in clinical situations which is characterized by "the typology of cases and the analogy as an inference method". A case is the unit of analysis. The structure of case was made with interaction of situation and moral rules. The situation is what surrounds or stands around. The moral rule is the essence of case. The analogy can be objective because "the grounds, the warrants, the theoretical backing, the modal qualifiers" are identified in the cases. The specified principlism was the method that Degrazia (1992) integrated the principlism and the specification introduced by Richardson (1990). In this method, the principle is specified by adding information about limitations of the scope and restricting the range of the principle. This should be substantive qualifications. The integrated method is an combination of the New Casuistry and the specified principlism. For example, the study was "Ethical problems experienced by nurses in the care of terminally ill patients"(Um, 1994). A semi-structured in-depth interview was conducted for fifteen nurses who mainly took care of terminally ill patients. The first stage, twenty one cases were identified as relevant to the topic, and then were classified to four types of problems. For instance, one of these types was the patient's refusal of care. The second stage, the ethical problems in the case were defined, and then the case was analyzed. This was to analyze the reasons, the ethical values, and the related ethical principles in the cases. Then the interpretation was synthetically done by integration of the result of analysis and the situation. The third stage was the ordering phase of the cases, which was done according to the result of the interpretation and the common principles in the cases. The first two stages describe the methodology of new casuistry, and the final stage was for the methodology of the specified principlism. The common principles were the principle of autonomy and the principle of caring. The principle of autonomy was specified; when competent patients refused care, nurse should discontinue the care to respect for the patients' decision. The principle of caring was also specified; when the competent patients refused care, nurses should continue to provide the care in spite of the patients' refusal to preserve their life. These specification may lead the opposite behavior, which emphasizes the importance of nurse's will and intentions to make their decision in the clinical situations.