• Journal of Information Technology Services
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• v.22 no.1
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• pp.43-56
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• 2023

With the development of the e-commerce market, various types of products continue to be released. However, customers face an information overload problem in purchasing decision-making. Therefore, personalized recommendations have become an essential service in providing personalized products to customers. Recently, many studies on GCN-based recommender systems have been actively conducted. Such a methodology can address the limitation in disabling to effectively reflect the interaction between customer and product in the embedding process. However, previous studies mainly use implicit feedback data to conduct experiments. Although implicit feedback data improves the data scarcity problem, it cannot represent customers' preferences for specific products. Therefore, this study proposed a novel model combining explicit and implicit feedback to address such a limitation. This study treats the average ratings of customers and products as the features of customers and products and converts them into a high-dimensional feature vector. Then, this study combines ID embedding vectors and feature vectors in the embedding layer to learn the customer-product interaction effectively. To evaluate recommendation performance, this study used the MovieLens dataset to conduct various experiments. Experimental results showed the proposed model outperforms the state-of-the-art. Therefore, the proposed model in this study can provide an enhanced recommendation service for customers to address the information overload problem.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.1015-1025
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• 2017

Bayesian personalized ranking (BPR) is a state-of-the-art recommendation system techniques for implicit feedback data. Unfortunately, there might be a loss of information because the BPR model considers only the binary transformation of implicit feedback that is non-binary data in most cases. We propose a modified BPR method using a level of confidence based on the size or strength of implicit feedback to overcome this limitation. The proposed method is useful because it still has a structure of interpretable models for underlying personalized ranking i.e., personal pairwise preferences as in the BPR and that it is capable to reflect a numerical size or the strength of implicit feedback. We propose a computation algorithm based on stochastic gradient descent for the numerical implementation of our proposal. Furthermore, we also show the usefulness of our proposed method compared to ordinary BPR via an analysis of steam video games data.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.641-642
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• 2008

In this paper, we propose a new approach for the implicit rating algorithm of finding user's intense and preference to the contents on the web. Although the explicit method dig out the user preference of specific contents based on the user's intervention, we propose the implicit method obtaining the user preference according to the user's behavioral patterns on the web implicitly and automatically without the user's intervention. The implementation results show that the proposed approach is highly valuable for supporting recommender systems in conjunction with the users lifestyle.

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1561-1566
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• 2017

Due to the advances in machine learning and artificial intelligence technologies, many new services have become available. Among such services, recommendation systems have already been successfully applied to commercial services and made profits as in online shopping malls. Most recommendation algorithms in commercial services are based on content analysis or explicit feedback rates as in movie recommendations. However, many online shopping malls have difficulties in content analysis or are lacking explicit feedbacks on their items, which results in no recommendation system for their items. Even for such service systems, user log data is easily available, and if recommendations are possible with such log data, the quality of their service can be improved. In this paper, we extract implicit feedback like click information for items from log data and provide a recommendation system based on the implicit feedback. The proposed system is applied to a real in-service online shopping mall.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.4
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• pp.361-370
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• 1992

A controller of the hovering VTOL aircraft with four fan is constructed by SRFIMF(State Rate Feedback Implicit Model-Following)theory, in which feedback state are angle acceleration, angle velocity and angle position of the aircraft during hover With yaw control of the system, characteristics of the hovering aircraft can be analyzed by changing states feedback gain and sponse provides robust stable hovering system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2381-2399
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• 2019

Traditional recommendation algorithms on Collaborative Filtering (CF) mainly focus on the rating prediction with explicit ratings, and cannot be applied to the top-N recommendation with implicit feedbacks. To tackle this problem, we propose a new collaborative filtering approach namely Maximize MAP with Matrix Factorization (MFMAP). In addition, in order to solve the problem of non-smoothing loss function in learning to rank (LTR) algorithm based on pairwise, we also propose a smooth MAP measure which can be easily implemented by standard optimization approaches. We perform experiments on three different datasets, and the experimental results show that the performance of MFMAP is significantly better than other recommendation approaches.

• English Language & Literature Teaching
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• v.17 no.1
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• pp.81-99
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• 2011

The role of corrective feedback (CF) has long been discussed in the field of second language acquisition. It has been claimed that CF enables learners to notice the problems in their second language (L2) production. However, it should not be assumed that learners always adequately interpret teachers' responses to their problematic utterance as correction. Especially when feedback is provided in an implicit way, the possibility that CF goes unnoticed should not be excluded. In this regard, the study aims to investigate how learners perceive teachers' corrective feedback in English classrooms in Korea. The study focuses particularly on examining the relationship between type of feedback and target linguistic content with learner interpretation of teacher corrective intention. Nine classrooms were observed and videotaped. Forty-five students and nine teachers participated in stimulated recall interviews. Their comments were analyzed to document the learners' perception and the teachers' intention of feedback. It was found that learner perception of teacher corrective intention was at its greatest when feedback was provided explicitly and was focused on morphological errors.

• Journal of Korea Multimedia Society
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• v.21 no.11
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• pp.1353-1361
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• 2018

With the availability of real-time educational data collection and analysis techniques, the education paradigm is shifting from educator-centric to data-driven lectures. However, most offline and online education frameworks collect students' feedback from question-answering data that can summarize their understanding but requires instructor's attention when students need additional help during lectures. This paper proposes a content restructure recommendation framework based on collected student feedback. We list the types of student feedback and implement a web-based framework that collects both implicit and explicit feedback for content restructuring. With a case study of four-week lectures with 50 students, we analyze the pattern of student feedback and quantitatively validate the effect of the proposed content restructuring measured by the level of student engagement.

• Journal of the Korean Society for Library and Information Science
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• v.35 no.3
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• pp.173-189
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• 2001

This paper presents a framework for observable behavior that can be used as a basis for user modeling, and it reports the results of a pair of user studies that examine the joint utility of two specific behaviors. User models can be constructed by hand, or they can be teamed automatically based on feedback provided by the user about the relevance of documents that they have examined. By observing user behavior, it is possible to obtain implicit feedback without requiring explicit relevance judgments. Four broad categories of potentially observable behavior are identified : examine, retain, reference, and annotate, and examples of specific behaviors within a category are further subdivided based on the natural scope of information objects being manipulated . segment object, or class. Previous studies using Internet discussion groups (USENET news) have shown reading time to be a useful source of implicit feedback for predicting a user's preferences. The experiments reported in this paper extend that work to academic and professional journal articles and abstracts, and explore the relationship between printing behavior and reading time. Two user studies were conducted in which undergraduate students examined articles or abstracts from the telecommunications or pharmaceutical literature. The results showed that reading time can be used to predict the user's assessment of relevance, that the mean reading time for journal articles and technical abstracts is longer than has been reported for USENET news documents, and that printing events provide additional useful evidence about relevance beyond that which can be inferred from reading time. The paper concludes with a brief discussion of the implications of the reported results.

• Journal of the Korean Society for information Management
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• v.41 no.1
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• pp.261-282
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• 2024

This study proposed and evaluated electroencephalography (EEG)-based and eye-tracking-based methods to determine relevance by utilizing users' implicit relevance feedback while navigating content in a digital library. For this, EEG/eye-tracking experiments were conducted on 32 participants using video, image, and text data. To assess the usefulness of the proposed methods, deep learning-based artificial intelligence (AI) techniques were used as a competitive benchmark. The evaluation results showed that EEG component-based methods (av_P600 and f_P3b components) demonstrated high classification accuracy in selecting relevant videos and images (faces/emotions). In contrast, AI-based methods, specifically object recognition and natural language processing, showed high classification accuracy for selecting images (objects) and texts (newspaper articles). Finally, guidelines for implementing a digital library interface based on EEG, eye-tracking, and artificial intelligence technologies have been proposed. Specifically, a system model based on implicit relevance feedback has been presented. Moreover, to enhance classification accuracy, methods suitable for each media type have been suggested, including EEG-based, eye-tracking-based, and AI-based approaches.