• 제목/요약/키워드: AI recommendations

검색결과 53건 처리시간 0.023초

브랜드 명성과 사회비교경향성이 AI 추천 제품의 브랜드 태도 및 구매의도 미치는 영향연구 (The Effects of Brand Repuration and Social Comparison on Consumers' Brand Attitude and Purchase Intention of a Product Recommended by AI)

  • 이성미
    • 스마트미디어저널
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    • 제13권1호
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    • pp.67-75
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    • 2024
  • 최근 검색사이트의 인공지능 기반 제품 추천 서비스의 도입이 늘어나고 있는 추세이다. 본 연구는 인공지능 기반 제품 추천에 대한 소비자의 반응 및 광고효과를 이해하고자 브랜드의 특성과 소비자 성향에 따라 AI 상품추천에 대한 소비자의 반응이 어떻게 달라지는지 연구하였다. 본 연구는 대학생을 대상으로 실험연구를 진행하였으며 브랜드 명성(높음 vs.낮음)과 비교성향수준(높음 vs.낮음)이 AI가 추천한 상품의 브랜드태도 및 구매의향에 미치는 영향을 검증하였다. 본 연구 결과 브랜드 명성과 비교성향 수준은 AI가 추천한 상품의 브랜드 태도에 영향을 미치는 것으로 나타났다. 본 연구의 결과는 AI 상품추천 서비스에 대한 소비자의 반응을 보다 세부적으로 이해하고 효과에 영향을 미치는 잠재적 요인들을 검증하였으며 AI를 활용한 서비스 전략 수립에 유용한 시사점을 제공하는 데 의의가 있다.

Assessing Personalized Recommendation Services Using Expectancy Disconfirmation Theory

  • Il Young Choi;Hyun Sil Moon;Jae Kyeong Kim
    • Asia pacific journal of information systems
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    • 제29권2호
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    • pp.203-216
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    • 2019
  • There is an accuracy-diversity dilemma with personalized recommendation services. Some researchers believe that accurate recommendations might reinforce customer satisfaction. However, others claim that highly accurate recommendations and customer satisfaction are not always correlated. Thus, this study attempts to establish the causal factors that determine customer satisfaction with personalized recommendation services to reconcile these incompatible views. This paper employs statistical analyses of simulation to investigate an accuracy-diversity dilemma with personalized recommendation services. To this end, we develop a personalized recommendation system and measured accuracy, diversity, and customer satisfaction using a simulation method. The results show that accurate recommendations positively affected customer satisfaction, whereas diverse recommendations negatively affected customer satisfaction. Also, customer satisfaction was associated with the recommendation product size when neighborhood size was optimal in accuracy. Thus, these results offer insights into personalizing recommendation service providers. The providers must identify customers' preferences correctly and suggest more accurate recommendations. Furthermore, accuracy is not always improved as the number of product recommendation increases. Accordingly, providers must propose adequate number of product recommendation.

초거대 인공지능 정책 변동과정에 관한 연구 : 옹호연합모형을 중심으로 (A Study on the Process of Policy Change of Hyper-scale Artificial Intelligence: Focusing on the ACF)

  • 최석원;이주연
    • 시스템엔지니어링학술지
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    • 제18권2호
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    • pp.11-23
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    • 2022
  • Although artificial intelligence(AI) is a key technology in the digital transformation among the emerging technologies, there are concerns about the use of AI, so many countries have been trying to set up a proper regulation system. This study analyzes the cases of the regulation policies on AI in USA, EU and Korea with the aim to set up and improve proper AI policies and strategies in Korea. In USA, the establishment of the code of ethics for the use of AI is led by private sector. On the other side, Europe is strengthening competitiveness in the AI industry by consolidating regulations that are dispersed by EU members. Korea has also prepared and promoted policies for AI ethics, copyright and privacy protection at the national level and trying to change to a negative regulation system and improve regulations to close the gap between the leading countries and Korea in AI. Moreover, this study analyzed the course of policy changes of AI regulation policy centered on ACF(Advocacy Coalition Framework) model of Sabatier. Through this study, it proposes hyper-scale AI regulation policy recommendations for improving competitiveness and commercialization in Korea. This study is significant in that it can contribute to increasing the predictability of policy makers who have difficulties due to uncertainty and ambiguity in establishing regulatory policies caused by the emergence of hyper-scale artificial intelligence.

The Ethics of AI in Online Marketing: Examining the Impacts on Consumer privacyand Decision-making

  • Preeti Bharti;Byungjoo Park
    • International Journal of Internet, Broadcasting and Communication
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    • 제15권2호
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    • pp.227-239
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    • 2023
  • Online marketing is a rapidly growing industry that heavily depends on digital technologies and data analysis to effectively reach and engage consumers. For that, artificial intelligence (AI) has emerged as a crucial tool for online marketers, enabling marketers to analyze extensive consumer data and automate decision-making processes. The purpose of this study was to investigate the ethical implications of using AI in online marketing, focusing on its impact on consumer privacy and decision-making. AI has created new possibilities for personalized marketing but raises concerns about the collection and use of consumer data, transparency and accountability of decision-making, and the impact on consumer autonomy and privacy. In this study, we reviewed the relevant literature and case studies to assess the potential risks and make recommendations for improving consumer protection. The findings provide insights into ethical considerations and offer a roadmap for balancing the advantages of AI in online marketing with the protection of consumer rights. Companies should consider these ethical issues when implementing AI in their marketing strategies. In this study, we explored the concerns and provided insights into the challenges posed by AI in online marketing, such as the collection and use of consumer data, transparency, and accountability of decision-making, and the impact on consumer autonomy and privacy.

Research on the Strategic Use of AI and Big Data in the Food Industry to Drive Consumer Engagement and Market Growth

  • Taek Yong YOO;Seong-Soo CHA
    • 식품보건융합연구
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    • 제10권1호
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    • pp.1-6
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    • 2024
  • Purpose: The research aims to address the intricacies of AI and Big Data application within the food industry. This study explores the strategic implementation of AI and Big Data in the food industry. The study seeks to understand how these technologies can be employed to bolster consumer engagement and contribute to market expansion, while considering ethical implications. Research Method: This research employs a comprehensive approach, analyzing current trends, case studies, and existing academic literature. It focuses on the application of AI and Big Data in areas such as supply chain management, consumer behavior analysis, and personalized marketing strategies. Results: The study finds that AI and Big Data significantly enhance market analytics, consumer personalization, and market trend prediction. It highlights the potential of these technologies in creating more efficient supply chains, improving consumer satisfaction through personalization, and providing valuable market insights. Conclusion and Implications: The paper offers actionable insights and recommendations for the effective implementation of AI and Big Data strategies in the food industry. It emphasizes the need for ethical considerations, particularly in data privacy and the transparency of AI algorithms. The study also explores future trends, suggesting that AI and Big Data will continue to revolutionize the industry, emphasizing sustainability, efficiency, and consumer-centric practices.

인공지능 기반 사회에서의 정보 격차 양상과 사회적 포용에 관한 미래 전망 연구 (A Prospective Study on the Aspects of the Digital Divide and Social Inclusion in an AI-based Society)

  • 차석기;정도범;서봉군
    • 지식경영연구
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    • 제25권3호
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    • pp.173-200
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    • 2024
  • 디지털 혁신과 인공지능(AI) 기술의 발전은 사회 전반에 걸쳐 큰 변화를 가져오고 있다. 본 연구는 인공지능이 점점 더 큰 영향을 미칠 2035년의 사회에서 정보 격차와 사회적 포용의 양상을 조사하였다. 이를 위해, 본 연구에서는 2×2 매트릭스 시나리오 분석 방법론을 사용하여 AI 기술 발전 수준과 사회적 대응 수준이라는 두 가지 주요 축을 기반으로 다양한 미래 시나리오를 도출하였다. 특히, 그리드 접근 방식을 통해 도출된 시나리오 간의 상호작용과 점진적 변화 가능성을 탐색함으로써, 인공지능 기반 사회에서의 정보 격차와 사회적 포용에 관한 다각적인 전망을 제시하고자 하였다. 본 연구는 AI 기반 사회에서 정보 격차 문제를 해결하기 위해 기술적 및 사회적 전략이 모두 중요하다는 점을 강조한다. 연구 결과는 정책 입안자와 이해관계자에게 AI가 사회적 포용과 정보 격차에 미치는 다차원적 영향을 알리고, 균형 잡힌 AI 미래를 위한 실행 가능한 전략을 수립하는 데 도움이 되기를 기대한다.

A Study on the Development of a Chatbot Using Generative AI to Provide Diets for Diabetic Patients

  • Ha-eun LEE;Jun Woo CHOI;Sung Lyul PARK;Min Soo KANG
    • 한국인공지능학회지
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    • 제12권3호
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    • pp.25-31
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    • 2024
  • The purpose of this study is to develop a sophisticated web-based artificial intelligence chatbot system designed to provide personalized dietary service for diabetic patients. According to a 2022 study, the prevalence of diabetes among individuals over 30 years old was 15.6% in 2020, identifying it as a significant societal issue with an increasing patient population. This study uses generative AI algorithms to tailor dietary recommendations for the elderly and various social classes, contributing to the maintenance of healthy eating habits and disease prevention. Through meticulous fine-tuning, the learning loss of the AI model was significantly reduced, nearing zero, demonstrating the chatbot's potential to offer precise dietary suggestions based on calorie intake and seasonal variations. As this technology adapts to diverse health conditions, ongoing research is crucial to enhance the accessibility of dietary information for the elderly, thereby promoting healthy eating practices and supporting disease prevention.

헬스케어 분야에서 활용 가능한 AI 기반 체형 3D 모델링 기술 개발 (Development of AI-Based Body Shape 3D Modeling Technology Applicable in The Healthcare Sector)

  • 이지용;김창균
    • 한국전자통신학회논문지
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    • 제19권3호
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    • pp.633-640
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    • 2024
  • 이 연구는 헬스케어 분야에서 활용 가능한 AI 기반의 3D 체형 모델링 기술을 개발하고, 이를 통해 사용자의 체형 변화와 건강 상태를 모니터링 할 수 있는 시스템을 제안한다. 사이즈코리아의 데이터를 활용하여 2D 이미지로부터 3D 체형 이미지를 생성하는 모델을 개발하고, 다양한 모델을 비교하여 가장 성능이 우수한 모델을 선정하였다. 최종적으로, 개발된 기술을 통해 개인 맞춤형 건강 관리, 운동 추천, 식단 제안 등의 시스템 프로세스를 제안함으로써 질병 예방 및 건강 증진에 기여하고자 하였다.

Knowledge Based Recommender System for Disease Diagnostic and Treatment Using Adaptive Fuzzy-Blocks

  • Navin K.;Mukesh Krishnan M. B.
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제18권2호
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    • pp.284-310
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    • 2024
  • Identifying clinical pathways for disease diagnosis and treatment process recommendations are seriously decision-intensive tasks for health care practitioners. It requires them to rely on their expertise and experience to analyze various categories of health parameters from a health record to arrive at a decision in order to provide an accurate diagnosis and treatment recommendations to the end user (patient). Technological adaptation in the area of medical diagnosis using AI is dispensable; using expert systems to assist health care practitioners in decision-making is becoming increasingly popular. Our work architects a novel knowledge-based recommender system model, an expert system that can bring adaptability and transparency in usage, provide in-depth analysis of a patient's medical record, and prescribe diagnostic results and treatment process recommendations to them. The proposed system uses a set of parallel discrete fuzzy rule-based classifier systems, with each of them providing recommended sub-outcomes of discrete medical conditions. A novel knowledge-based combiner unit extracts significant relationships between the sub-outcomes of discrete fuzzy rule-based classifier systems to provide holistic outcomes and solutions for clinical decision support. The work establishes a model to address disease diagnosis and treatment recommendations for primary lung disease issues. In this paper, we provide some samples to demonstrate the usage of the system, and the results from the system show excellent correlation with expert assessments.

New Approaches to Assessing Nutrient Intakes Using the Dietary Reference Intakes

  • Murphy, Suzanne P.
    • Nutritional Sciences
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    • 제6권1호
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    • pp.48-52
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    • 2003
  • The Dietary Reference Intakes (DRI's) are new nutrient intake standards that are being set for the United States and Canada. There are currently four types of DRI's: Estimated Average Requirements (EAR), Recommended Dietary Allowances (RDA), Adequate Intakes (AI), and Tolerable Upper Intake Levels (UL). The EAR is the nutrient intake that would be adequate for about half the population, while intake at the RDA should be adequate for 97-98% of the population. When the data are insufficient to set an EAR and RDA, then an AI is set. The UL is the highest intake level that does not pose a risk of adverse effects. The EAR, AI, and UL may be used to assess intakes of both individuals and of groups of people. For individuals, the EAR is used to calculate the probability that intake is inadequate, the AI is used to decide if the probability of inadequacy is low, and the UL is used to determine if a risk of excess intake is present. For groups. the EAR is used to estimate the prevalence of inadequacy, the AI is used to decide if the prevalence of inadequacy is low, and the UL is used to estimate the prevalence of excessive intakes. Because this approach to setting and applying nutrient standards is new, research recommendations include improving estimates of risk, improving dietary data, and improving statistical methods.