• Asia pacific journal of information systems
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• v.29 no.4
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• pp.856-874
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• 2019

The performance of recommender systems (RS) has been measured mainly in terms of accuracy. However, there are other aspects of performance that are difficult to understand in terms of accuracy, such as coverage, serendipity, and satisfaction with recommended results. Moreover, particularly with RSs that suggest multiple items at a time, such as YouTube, user satisfaction with recommended results may vary not only depending on their accuracy, but also on their configuration, content, and design displayed to the user. This is true when classifying an RS as a single RS with one recommended result and as a multiple RS with diverse results. No empirical analysis has been conducted on the influence of the content and distribution of recommendation items on user satisfaction. In this study, we propose a research model representing the content and distribution of recommended items and how they affect user satisfaction with the RS. We focus on RSs that recommend multiple items. We performed an empirical analysis involving 149 YouTube users. The results suggest that user satisfaction with recommended results is significantly affected according to the HHI (Herfindahl-Hirschman Index). In addition, satisfaction significantly increased when the recommended item on the top of the list was the same category in terms of content that users were currently watching. Particularly when the purpose of using RS is hedonic, not utilitarian, the results showed greater satisfaction when the number of views of the recommended items was evenly distributed. However, other characteristics of selected content, such as view count and playback time, had relatively less impact on satisfaction with recommended items. To the best of our knowledge, this study is the first to show that the category concentration of items impacts user satisfaction on websites recommending diverse items in different categories using a content-based filtering system, such as YouTube. In addition, our use of the HHI index, which has been extensively used in economics research, to show the distributional characteristics of recommended items, is also unique. The HHI for categories of recommended items was useful in explaining user satisfaction.

• Journal of Information Technology Applications and Management
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• v.21 no.4
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• pp.203-223
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• 2014

This study proposes a novel context-aware recommender system, which is designed to recommend the items according to the customer's responses to the previously recommended item. In specific, our proposed system predicts the user's emotional state from his or her responses (such as facial expressions and movements) to the previous recommended item, and then it recommends the items that are similar to the previous one when his or her emotional state is estimated as positive. If the customer's emotional state on the previously recommended item is regarded as negative, the system recommends the items that have characteristics opposite to the previous item. Our proposed system consists of two sub modules-(1) emotion prediction module, and (2) responsive recommendation module. Emotion prediction module contains the emotion prediction model that predicts a customer's arousal level-a physiological and psychological state of being awake or reactive to stimuli-using the customer's reaction data including facial expressions and body movements, which can be measured using Microsoft's Kinect Sensor. Responsive recommendation module generates a recommendation list by using the results from the first module-emotion prediction module. If a customer shows a high level of arousal on the previously recommended item, the module recommends the items that are most similar to the previous item. Otherwise, it recommends the items that are most dissimilar to the previous one. In order to validate the performance and usefulness of the proposed recommender system, we conducted empirical validation. In total, 30 undergraduate students participated in the experiment. We used 100 trailers of Korean movies that had been released from 2009 to 2012 as the items for recommendation. For the experiment, we manually constructed Korean movie trailer DB which contains the fields such as release date, genre, director, writer, and actors. In order to check if the recommendation using customers' responses outperforms the recommendation using their demographic information, we compared them. The performance of the recommendation was measured using two metrics-satisfaction and arousal levels. Experimental results showed that the recommendation using customers' responses (i.e. our proposed system) outperformed the recommendation using their demographic information with statistical significance.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.803-813
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• 2013

The purchase of items in e-commerce is a little bit different from that of items in off-line. The recommendation of items in off-line is conducted by salespersons' recommendation, However, the item recommendation in e-commerce cannot be recommended by salespersons, and so different types of methods can be recommended in e-commerce. Recommender system is a method which recommends items in e-commerce. Preferences of customers who want to purchase new items can be predicted by the preferences of customers purchasing existing items. In the recommender system, the items with estimated high preferences can be recommended to customers. The algorithm of collaborative filtering is used in recommender system of e-commerce, and the list of recommended items is made by estimated values, and then the list is recommended to customers. The dataset used in this research are 100k dataset and 1 million dataset in Movielens dataset. Similar results in two dataset are deducted for generalization. To suggest a new algorithm, distribution features of estimated values are analyzed by the existing algorithm and transformed algorithm. In addition, respondent'distribution features are analyzed respectively. To improve the collaborative filtering algorithm in neighborhood recommender system, a new algorithm method is suggested on the basis of existing algorithm and transformed algorithm.

• Proceedings of the Membrane Society of Korea Conference
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• 1997.06a
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• pp.1-20
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• 1997

The present standard of drinking water quality is not reached to the guidelines of WHO and US EPA recommended. The appraisal of safety is not appropriate by the results of intermittent and limitted analysis. 45 items of drinking water quality are regulated in the Korean standard and 9 items for inspection designated by Seoul City. This report is the results of analysis of the water quality in the water stations of Seoul which are concerned with the items of Korean water quality standard and the priolity pollutnats recommended by WHO. In the results, 45 items of water quality, and the priolity pollutants were not exceeded to the standard and criteria, while DDT, heptachlor-epoxide, THMs, benzo(a)pyrene, Ba, Al, Gross beta, $^{226}$Ra, $^{90}$Sr were detected, the levels were not exceeded to the WHO guidelines. In ordes to evalute the safety of drinking water quality, besides of the existed items of standard, new hazardouse pollutants should be considered monitored continenously. For the regulation of hazardous pollutants, it may be introduced from the risk assessment. According to the relevant assessment, the acceptable risk of pollutants estimated could be applied to set the water quality standard or recommendations or quidelines as well as the number of monitoring.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.11a
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• pp.602-609
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• 2007

Combining collaborative filtering with some other technique is most common in hybrid recommender systems. As many recommended items from collaborative filtering seem to be similar with respect to content, the collaborative-content hybrid system suffers in terms of quality recommendation and recommending new items as well. To alleviate such problem, we have developed a novel method that uses a diversity metric to select the dissimilar items among the recommended items from collaborative filtering, which together with the input when fed into content space let us improve and include new items in the recommendation. We present experimental results on movielens dataset that shows how our approach performs better than simple content-based system and naive hybrid system

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.2
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• pp.128-136
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• 2015

With the recent interests with culture technologies, many studies for recommendation systems have been done. In this vein, various music recommendation systems have been developed. However, they have often focused on the technical aspects such as feature extraction and similarity comparison, and have not sufficiently addressed them in user-centered perspectives. For users' high satisfaction with recommended music items, it is necessary to study how the items are connected to the users' actual desires. For this, our study proposes a novel music recommendation method based on serendipity, which means the freshness users feel for their familiar items. The serendipity is measured through the comparison of users' past and recent listening tendencies. We utilize neural networks to apply these tendencies to the recommendation process and to extract the features of music items as MFCCs (Mel-frequency cepstral coefficients). In that the recommendation method is developed based on the characteristics of user behaviors, it is expected that user satisfaction for the recommended items can be actually increased.

• Proceedings of the KSR Conference
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• 2001.05a
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• pp.200-206
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• 2001

In the standard specifications for the urban EMU(Electric Multiple Unit) train, there are several items to ensure safety against accidents. The 21th -23th items have much relation with the crashworthiness of the urban EMU train. In this study, the rubber- tired AGT(Automated Guide-way Transit System) under development by KRRI is numerically evaluated in a crashworthy point of view by applying the above crashworthiness items. The numerical results show the detail design of the AGT satisfies the 22th and 23th items. But the design is recommended to adopt mechanical fuses to reduce the impact accelerations with respect to the 21th item.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1242-1250
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• 2022

When recommending cold items that do not have user-item interactions to users, even we adopt state-of-the-arts algorithms, the predicted information of cold items tends to have lower accuracy compared to warm items which have enough user-item interactions. The lack of information makes for recommender systems to recommend monotonic items which have a few top popular contents matched to user preferences. As a result, under-diversified items have a negative impact on not only recommendation diversity but also on recommendation accuracy when recommending cold items. To address the problem, we adopt a diversification algorithm which tries to make distributions of accumulated contents embedding of the two items groups, recommended items and the items in the target user's already interacted items, similar. Evaluation on a real world data set CiteULike shows that the proposed method improves not only the diversity but also the accuracy of cold item recommendation.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.127-141
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• 2017

Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.

• Spatial Information Research
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• v.6 no.2
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• pp.169-182
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• 1998

The Zone-Based. landuse Information System(ZBLIS) is an useful information system for city administrators and researchers for planning and analysis. The purpose of this study is to establish concept and methodology of the ZBLIS. The concept of Planning Analysis Zone(PAZ), as a basic unit of analysis, was defined to establish the ZBLIS. Then, various PAZs were suggested through a case study of Pohang city. Also, various useful attribute data were recommended in this study for ZBLIS. Digital Maps were used to establish ZBLIS. Among 730 subcategory items in the Digital Maps, some necessary items were selected for ZBLIS. A 84 items was recommended to be use of the ZBLIS. And 250 more items were recommended for more detailed analysis of the ZBLIS. While previous studies of landuse infonnatiom systems have focused on establishing Parcel-based Landuse Infonnation Systems, it is meaningful that this study is trying to recognize the significance of the ZBLIS and suggest the concept and methodology of the ZBLIS.