• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.469-476
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• 2022

As the 3D modeling technology and conversational algorithm is developed, digital humans are being used in various fields, and also virtual bankers have begun to appear in banks, including major banks such as Shin-Han Bank and Nong-Hyup Bank. However, most of the research of digital human mainly focus on its appearance, and research on robot persona that should be considered in anthropomorphizing a robot is insufficient. In this study, an experiment was conducted to find out the user experience of three scenarios (student ID receipt, deposit and withdrawal account opening, leasehold loan consultation) in which the level of anthropomorphism of the speech strategy and the level of personal information use differed in the specific context of banking. As a result of the study, social presence and usefulness had an interactive effect on the scenario and the level of anthropomorphism. There was no interaction effect on intimacy, trustworthiness, and perceived intelligence, but a tendency could be confirmed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1396-1412
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• 2023

Conversation modeling is an important and challenging task in the field of natural language processing because it is a key component promoting the development of automated humanmachine conversation. Most recent research concerning conversation modeling focuses only on the current utterance (considered as the current question) to generate a response, and thus fails to capture the conversation's logic from its beginning. Some studies concatenate the current question with previous conversation sentences and use it as input for response generation. Another approach is to use an encoder to store all previous utterances. Each time a new question is encountered, the encoder is updated and used to generate the response. Our approach in this paper differs from previous studies in that we explicitly separate the encoding of the question from the encoding of its context. This results in different encoding models for the question and the context, capturing the specificity of each. In this way, we have access to the entire context when generating the response. To this end, we propose a deep neural network-based model, called the Context Model, to encode previous utterances' information and combine it with the current question. This approach satisfies the need for context information while keeping the different roles of the current question and its context separate while generating a response. We investigate two approaches for representing the context: Long short-term memory and Convolutional neural network. Experiments show that our Context Model outperforms a baseline model on both ConvAI2 Dataset and a collected dataset of conversational English.

• Journal of Fashion Business
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• v.27 no.3
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• pp.50-62
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• 2023

The e-commerce fashion market has experienced a remarkable growth, leading to an overwhelming availability of shared information and numerous choices for users. In light of this, chatbots have emerged as a promising technological solution to enhance personalized services in this context. This study aimed to develop user-product attributes for a chatbot-based personalized fashion recommendation service using big data text mining techniques. To accomplish this, over one million consumer reviews from Coupang, an e-commerce platform, were collected and analyzed using frequency analyses to identify the upper-level attributes of users and products. Attribute terms were then assigned to each user-product attribute, including user body shape (body proportion, BMI), user needs (functional, expressive, aesthetic), user TPO (time, place, occasion), product design elements (fit, color, material, detail), product size (label, measurement), and product care (laundry, maintenance). The classification of user-product attributes was found to be applicable to the knowledge graph of the Conversational Path Reasoning model. A testing environment was established to evaluate the usefulness of attributes based on real e-commerce users and purchased product information. This study is significant in proposing a new research methodology in the field of Fashion Informatics for constructing the knowledge base of a chatbot based on text mining analysis. The proposed research methodology is expected to enhance fashion technology and improve personalized fashion recommendation service and user experience with a chatbot in the e-commerce market.

• Journal of Korea Technology Innovation Society
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• v.22 no.2
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• pp.242-264
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• 2019

This study focuses on considering the factors affecting the user intention to adopt an intelligent service - A.I. speaker services. Currently there can be a considerable difference between the expectation and the realized diffusion of IT-based intelligent services. This study aims to find out this gap based on the idea of diver previous researches including TAM and UTAUT studies and to identify the direct and indirect effects of diverse factors such as security issues, perceived time pressure, service innovativeness, and the experience of these IT-based intelligent services. And this study considers the expected impact of perceived time pressure factor on the user acceptance of A.I. speaker services. In analysis results, not only the traditional factors such as the perceived usefulness and the hedonic/utilitarian motives but also the perceived time pressure, the perceived security issues, and the experience of the services should be considered as meaningful factors to affect the users adopting A.I. speaker services.