• Journal of the Korean Society for information Management
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• v.20 no.4 s.50
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• pp.23-48
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• 2003

In this study an information filtering system was implemented and a series of relevance feedback experiments were conducted using the system. For the relevance feedback, the original queries were searched against the database and the results were reviewed by the researchers. Based on users' online relevance judgements a pair of 17 refined queries were generated using two methods called 'co-occurrence exclusion method' and 'lower frequencies exclusion method,' In order to generate them, the original queries, the descriptors and category codes appeared in either relevant or irrelevant document sets were applied as elements. Users' relevance judgments on the search results of the refined queries were compared and analyzed against those of the original queries.

• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1832-1840
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• 1999

Since the existed Korean Web IR systems generally use boolean system, it is difficult to retrieve the information to be wanted at one time. Also, because of the feature that web documents have the frequent abbreviation and many links, the keyword extraction using the inverted document frequency extracts the improper keywords for adding ambiguous meaning problem. Therefore, users must repeat the modification of the queries until they get the proper information. In this paper, we design and implement the relevance feedback agent system for resolving the above problems. The relevance feedback agent system extracts the proper information in response to user's preferred keywords and stores these keywords in preference DB table. When users retrieve this information later, the relevance feedback agent system will search it adding relevant keywords to user's queries. As a result of this method, the system can reduce the number of modification of user's queries and improve the efficiency of the IR system.

• Smart Media Journal
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• v.12 no.9
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• pp.89-94
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• 2023

Recommender systems provide users with the most favorable items by analyzing explicit or implicit feedback of users on items. Recently, as the size of deep-learning-based models employed in recommender systems has increased, many studies have focused on reducing inference time while maintaining high recommendation accuracy. As one of them, a study on recommender systems with a knowledge distillation (KD) technique is actively conducted. By KD, a small-sized model (i.e., student) is trained through knowledge extracted from a large-sized model (i.e., teacher), and then the trained student is used as a recommendation model. Existing studies on KD for recommender systems have been mainly performed only for implicit feedback settings. Thus, in this paper, we try to investigate the performance and accuracy when applied to explicit feedback settings. To this end, we leveraged a total of five state-of-the-art KD methods and three real-world datasets for recommender systems.

• ETRI Journal
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• v.36 no.1
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• pp.62-68
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• 2014

This paper presents a novel user selection method based on the signal-to-interference-plus-noise ratio (SINR), which is approximated using limited feedback data at the base stations (BSs) of multiple user multiple-input multiple-output (MU-MIMO) systems. In the proposed system, the codebook vector index, the quantization error obtained from the correlation between the measured channel and the codebook vector, and the measured value of the largest singular value are fed back from each user to the BS. The proposed method not only generates precoding vectors that are orthogonal to the precoding vectors of the previously selected users and are highly correlated with the codebook vector of each user but also adopts the quantization error in approximating the SINR, which eventually provides a significantly more accurate SINR than the conventional SINR-based user selection techniques. Computer simulations show that the proposed method enhances the sum rate of the conventional SINR-based methods by at least 2.4 (2.62) bps/Hz when the number of transmit antennas and number of receive antennas per user terminal is 4 and 1(2), respectively, with 100 candidate users and an SNR of 30 dB.

• Journal of Satellite, Information and Communications
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• v.10 no.2
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• pp.10-13
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• 2015

This paper proposed a novel user's information feedback algorithm for improving network energy harvesting rate. The proposed algorithm is focused on determining energy harvesting users comparing increasing ratio of the amount of harvesting energy versus emitted energy and network threshold ${\alpha}$, which is critical harvesting parameter. Using this method, we can increase the rate of network energy harvesting preventing emitted energy from wasting inefficiently. The result of experiment in this paper shows that user's information feedback algorithm makes network energy harvesting rate more efficiently when it uses threshold ${\alpha}=15%$ to determine energy harvesting users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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• pp.1292-1306
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• 2014

This paper proposes a space-polarization division multiple access (SPDMA) system that has limited feedback channels. The system simultaneously serves data streams to multiple mobile users through dual-polarized antenna arrays, by using pre-determined sets of precoding vectors that are orthogonal in both space and polarization domains. To this end, a codebook whose elements are sets of the precoding vectors is systematically designed based on the discrete Fourier transform (DFT) matrix and considering the power imbalance of polarized channels. Throughput of the SPDMA system is evaluated and compared to that of space division multiple access (SDMA) system, according to the various parameters including cross polarization discrimination (XPD). The results show that the throughput of SPDMA system outperforms that of SDMA in the environments of high XPD with many mobile users.

• ETRI Journal
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• v.33 no.2
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• pp.240-250
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• 2011

To enable a relevance feedback paradigm to evolve itself by users' feedback, a reinforcement learning method is proposed. The feature space of the medical images is partitioned into positive and negative hypercubes by the system. Each hypercube constitutes an individual in a genetic algorithm infrastructure. The rules take recombination and mutation operators to make new rules for better exploring the feature space. The effectiveness of the rules is checked by a scoring method by which the ineffective rules will be omitted gradually and the effective ones survive. Our experiments on a set of 10,004 images from the IRMA database show that the proposed approach can better describe the semantic content of images for image retrieval with respect to other existing approaches in the literature.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.419-425
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• 2024

As digital communication gains prominence, there is an increasing trend in uncivil behaviors like rude or hateful comments and the empathetic actions towards them, highlighting the need for social efforts to address these issues. As part of these endeavors, we investigated how avatar feedback in a virtual news portal affects users' empathy towards uncivil comments. We defined both posting and empathizing with uncivil comments as antisocial actions. To this end, we posted socially controversial news in a virtual space and provided feedback in two forms when participants selected uncivil comments: text-only feedback and feedback accompanied by an avatar. We then assessed the impact of this feedback on behavioral conformity, guilt, and self-image concern through surveys. Our results showed that avatar-provided feedback significantly influenced participants' social responses more than text-based feedback. Interaction with avatars notably increased participants' behavioral conformity, guilt, and self-image concern. We concluded that avatar-based interactions can positively influence users' social behaviors and attitudes, suggesting their potential in fostering a more civil and responsible digital communication culture.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.6A
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• pp.643-651
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• 2008

In this paper, we analyze the performance of normalized SNR based proportional fair scheduling with partial feedback information for multiuser MIMO-OFDMA systems. The closed form expression on the downlink capacity of the selective partial CQI feedback scheme is derived and its asymptotic behavior is investigated. From the performance analysis and numerical results, it is found that the optimal growth rate of downlink capacity can be achieved with bounded average feedback overhead irrespective of the number of users.

• Journal of the Korean Society for information Management
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• v.2 no.1
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• pp.108-135
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• 1985

When an information retrieval system is implemented on-line, users can interact with the system to improve the searches. There are studies which achieved dramatic improvements in system effectiveness by using automatic relevance feedback, a technique for reformulating a patron query based on initial retrieval result. In this thesis, an automatic query modification model was applied to a controlled keyword system.