• 제목/요약/키워드: 여행 추천

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Effects of AI Chatbot and Service Agent on Attitude and Choice Deferral of Recommended Products (AI 챗봇과 상담원이 추천하는 제품에 대한 태도와 선택연기에 미치는 영향)

  • Yoo, Kun-Woo
    • Journal of Digital Convergence
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    • v.20 no.3
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    • pp.297-307
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    • 2022
  • This study examined whether there was a difference in the attitude toward the recommended product and the effect on the choice deferral according to information sources. Experiment 1 examined the relationship between trust in information and product attitude, and between uncertainty and choice deferral according to information sources (AI chatbot vs. human). Experiment 2 examined the impact of social presence, perceived personalization, and choice deferral according to whether anthropomorphism of AI chatbots or not. The research results are as follows. First, consumers were found to have a more positive attitude toward products recommended by AI chatbots (vs. human). Second, consumers were more choice deferral whether to purchase products recommended by AI chatbots (vs. human). Third, it was found that consumers' selection of products recommended by anthropomorphic AI chatbots (vs. impersonated AI chatbots) increased. Also, the implications of this study and future research directions were discussed.

Design of Infant Care Contents Provision Web Page that Parents and Children Enjoy Together (부모와 유아가 함께 즐기는 육아 콘텐츠 제공 웹페이지 설계)

  • Do, Hyemi;Ham, Seeun;Gu, wenting;Park, Eunju;Lim, Hankyu
    • Proceedings of the Korea Information Processing Society Conference
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    • 2017.04a
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    • pp.411-414
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    • 2017
  • 부모와 유아가 함께 보내는 프로그램이 많아지면서 자연적으로 부모와 유아가 함께 보내는 시간에 대한 관심이 크게 증가하고 있다. 하지만 부모와 유아가 함께 할 수 있는 콘텐츠에 대한 정보 제공 서비스는 속도를 따라가지 못하고 있다. 이에 본 논문에서는 부모와 유아가 함께 즐길 수 있는 육아 콘텐츠정보 제공 웹페이지를 설계하고 구현하였다. 부모와 유아가 함께 할 수 있는 여행지 추천, 게임, 요리 레시피 제공을 통해서 부모와 유아가 함께 할 수 있는 콘텐츠를 제공함을 목적으로 하였다. 본 논문에서 구현한 웹 페이지의 사용으로 유아를 가진 부모들의 정보제공에 도움이 되기를 기대한다.

Intelligent Agent-based Travel Planning Recommendation System in Peak Seasons (지능형 소프트웨어 에이전트에 기반한 피크 기간에서의 여행 계획 추천 시스템)

  • Yim Hong Soon;Ahn Hyung Jun;Kim Jong Woo;Park Sung Joo
    • Korean Management Science Review
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    • v.21 no.3
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    • pp.39-54
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    • 2004
  • This paper presents a multi-agent system for intelligent recommendation of travel plans to users. The goal of the system is to provide alternative and preferable travel plans to users when the availability of tickets is low such as in vacations, holidays, weekends, or peak seasons. The multiple agents in the system search for available alternatives for a target travel in collaboration with other agents and recommend best alternatives by analyzing them using a multi-criteria decision-making model. A prototype online travel support system was constructed and a simulation experiment was performed for evaluation and comparison with different travel planning strategies.

Personalized Travel Path Recommendations with Social Life Log (소셜 라이프 로그를 이용한 개인화된 여행 경로 추천)

  • Paul, Aniruddha;Lim, Jongtae;Bok, Kyoungsoo;Yoo, Jasesoo
    • Proceedings of the Korea Contents Association Conference
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    • 2017.05a
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    • pp.453-454
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    • 2017
  • The travellers using social media leave their location history in the form of trajectories. These trajectories can be bridged for acquiring information, required for future recommendation for the future travelers, who are new to that location, providing all sort of information. In this paper, we propose a personalized travel path recommendation scheme based on social life log. By taking advantage of two kinds of social media such as travelogue and community contributed photos, the proposed scheme can not only be personalized to user's travel interest but also be able to recommend a travel path rather than individual Points of Interest (POIs). It also maps both user's and routes' textual descriptions to the topical package space to get user topical package model and route topical package model (i.e., topical interest, cost, time and season).

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Photo spot Recommendation System Based on GPS (GPS 기반의 포토 스팟 추천 시스템)

  • Jun-ho Choi;Ba-da Kim;Jang-hyun Mun;Chan-woo Kim;Jun-huck Lee;Jun-ho Park
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2024.01a
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    • pp.283-284
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    • 2024
  • 한국의 스마트폰 보유율은 약 95%로 세계 최고이며, 사용 시간이 가장 긴 애플리케이션의 대부분이 SNS이다. 코로나19로 여행객이 늘면서 사람들은 SNS를 통해 국내외 여행 사진을 게시 및 자랑한다. 그러나 기존 SNS에서는 지역별 사진을 찾기 어렵고 정확한 위치 표시가 부족하다. 이에 따라 원하는 사진과 위치를 확인하고 내비게이션 기능을 이용하여 목적지까지 갈 수 있는 앱을 개발하게 되었다. 국내 지도상의 주요 포토 스팟과 해당 지역의 사진을 확인할 수 있는 애플리케이션을 개발하여 사용자가 직접 참여하고 공유할 수 있도록 한다.

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A Study on the Effect of Booth Recommendation System on Exhibition Visitors Unplanned Visit Behavior (전시장 참관객의 계획되지 않은 방문행동에 있어서 부스추천시스템의 영향에 대한 연구)

  • Chung, Nam-Ho;Kim, Jae-Kyung
    • Journal of Intelligence and Information Systems
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    • v.17 no.4
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    • pp.175-191
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    • 2011
  • With the MICE(Meeting, Incentive travel, Convention, Exhibition) industry coming into the spotlight, there has been a growing interest in the domestic exhibition industry. Accordingly, in Korea, various studies of the industry are being conducted to enhance exhibition performance as in the United States or Europe. Some studies are focusing particularly on analyzing visiting patterns of exhibition visitors using intelligent information technology in consideration of the variations in effects of watching exhibitions according to the exhibitory environment or technique, thereby understanding visitors and, furthermore, drawing the correlations between exhibiting businesses and improving exhibition performance. However, previous studies related to booth recommendation systems only discussed the accuracy of recommendation in the aspect of a system rather than determining changes in visitors' behavior or perception by recommendation. A booth recommendation system enables visitors to visit unplanned exhibition booths by recommending visitors suitable ones based on information about visitors' visits. Meanwhile, some visitors may be satisfied with their unplanned visits, while others may consider the recommending process to be cumbersome or obstructive to their free observation. In the latter case, the exhibition is likely to produce worse results compared to when visitors are allowed to freely observe the exhibition. Thus, in order to apply a booth recommendation system to exhibition halls, the factors affecting the performance of the system should be generally examined, and the effects of the system on visitors' unplanned visiting behavior should be carefully studied. As such, this study aims to determine the factors that affect the performance of a booth recommendation system by reviewing theories and literature and to examine the effects of visitors' perceived performance of the system on their satisfaction of unplanned behavior and intention to reuse the system. Toward this end, the unplanned behavior theory was adopted as the theoretical framework. Unplanned behavior can be defined as "behavior that is done by consumers without any prearranged plan". Thus far, consumers' unplanned behavior has been studied in various fields. The field of marketing, in particular, has focused on unplanned purchasing among various types of unplanned behavior, which has been often confused with impulsive purchasing. Nevertheless, the two are different from each other; while impulsive purchasing means strong, continuous urges to purchase things, unplanned purchasing is behavior with purchasing decisions that are made inside a store, not before going into one. In other words, all impulsive purchases are unplanned, but not all unplanned purchases are impulsive. Then why do consumers engage in unplanned behavior? Regarding this question, many scholars have made many suggestions, but there has been a consensus that it is because consumers have enough flexibility to change their plans in the middle instead of developing plans thoroughly. In other words, if unplanned behavior costs much, it will be difficult for consumers to change their prearranged plans. In the case of the exhibition hall examined in this study, visitors learn the programs of the hall and plan which booth to visit in advance. This is because it is practically impossible for visitors to visit all of the various booths that an exhibition operates due to their limited time. Therefore, if the booth recommendation system proposed in this study recommends visitors booths that they may like, they can change their plans and visit the recommended booths. Such visiting behavior can be regarded similarly to consumers' visit to a store or tourists' unplanned behavior in a tourist spot and can be understand in the same context as the recent increase in tourism consumers' unplanned behavior influenced by information devices. Thus, the following research model was established. This research model uses visitors' perceived performance of a booth recommendation system as the parameter, and the factors affecting the performance include trust in the system, exhibition visitors' knowledge levels, expected personalization of the system, and the system's threat to freedom. In addition, the causal relation between visitors' satisfaction of their perceived performance of the system and unplanned behavior and their intention to reuse the system was determined. While doing so, trust in the booth recommendation system consisted of 2nd order factors such as competence, benevolence, and integrity, while the other factors consisted of 1st order factors. In order to verify this model, a booth recommendation system was developed to be tested in 2011 DMC Culture Open, and 101 visitors were empirically studied and analyzed. The results are as follows. First, visitors' trust was the most important factor in the booth recommendation system, and the visitors who used the system perceived its performance as a success based on their trust. Second, visitors' knowledge levels also had significant effects on the performance of the system, which indicates that the performance of a recommendation system requires an advance understanding. In other words, visitors with higher levels of understanding of the exhibition hall learned better the usefulness of the booth recommendation system. Third, expected personalization did not have significant effects, which is a different result from previous studies' results. This is presumably because the booth recommendation system used in this study did not provide enough personalized services. Fourth, the recommendation information provided by the booth recommendation system was not considered to threaten or restrict one's freedom, which means it is valuable in terms of usefulness. Lastly, high performance of the booth recommendation system led to visitors' high satisfaction levels of unplanned behavior and intention to reuse the system. To sum up, in order to analyze the effects of a booth recommendation system on visitors' unplanned visits to a booth, empirical data were examined based on the unplanned behavior theory and, accordingly, useful suggestions for the establishment and design of future booth recommendation systems were made. In the future, further examination should be conducted through elaborate survey questions and survey objects.

Tourist Map Services based on Mobile using Social Location Information (소셜 위치정보를 이용한 모바일 기반 관광지도 서비스)

  • Jang, Dong-Min;Chae, Seok-Woo;Kim, Min-Woo;Ryu, Jeong-Uook;Hwang, Se-Hee;Song, Jeo
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2016.07a
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    • pp.241-242
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    • 2016
  • 지난해 MERS 등의 영향으로 주춤했던 국내 관광산업이 올해에는 정부의 임시 공휴일 운영 및 대체 공휴일 실시 등과 함께 외국인 관광인구 유입 증가 등으로 MICE산업 형태로 확대되고 있다. 본 논문에서는 이러한 국내 관광지를 찾는 다양한 여행자에게 위치에 기반한 상세 관광 안내 모바일 서비스를 제공하고 다양한 이동경로를 갖을 수 있는 실외 관광지의 다중경로에 대하여 이용자 소셜 위치정보를 통해, 사용자별 맞춤형 관광경로 추천 및 최단경로 안내 방법에 대하여 제안한다.

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Hashtag Analysis Scheme for Topic based Tweet Categorization (토픽 기반의 트윗 분류를 위한 해시태그 분석 기법)

  • Kim, Yongsung;Jun, Sanghoon;Rew, Jehyeok;Hwang, Eenjun
    • Proceedings of the Korea Information Processing Society Conference
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    • 2014.11a
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    • pp.737-740
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    • 2014
  • 최근 SNS 사용자가 급증하면서 매우 다양하고 방대한 양의 글이 여러 종류의 SNS를 통해 생성되고 있다. 그중 트위터는 정보의 전달 및 확산에 상당히 유용한 도구로 사용되고 있다. 이러한 트위터의 사용자 트윗은 뉴스, 음악, 사진, 여행 등 다양한 형태로 등장한다. 또한 트위터는 해시태그라는 사용자 정의 태그를 사용하는데 이는 트윗의 키워드 및 핵심을 쉽게 표현할 수 있도록 해주는 효과적인 수단이다. 최근 상당히 많은 양의 트윗의 생성에도 불구하고 이를 다양한 카테고리별로 분류할 수 있는 연구가 많이 진행되지 않았다. 따라서 본 논문에서는 해시태그를 이용해 트윗의 핵심을 파악하고 수많은 트윗을 다양한 토픽별로 분류할 수 있는 기법을 제안한다. 우선 다양한 카테고리의 인기 해시태그가 포함된 트윗을 수집하고 수집한 트윗에서 해시태그별 키워드를 추출한다. 그리고 코사인 유사도를 통해 해시태그별 내용 유사도를 파악하여 각 카테고리 내의 해시태그가 얼마나 유사한 내용을 지니고 있는지 파악한다. 마지막으로 사용자 트윗이 입력되면 모든 카테고리와 유사도를 비교하여 가장 유사도가 높은 카테고리를 찾아 추천해준다. 제안된 기법을 바탕으로 프로토타입을 구현하고 실험을 통해 성능을 평가한다.

CYTRIP: A Multi-day Trip Planning System based on Crowdsourced POIs Recommendation (CYTRIP: 크라우드 소싱을 이용한 POI 추천 기반의 여행 플래닝 시스템)

  • Aprilia, Priska;Oh, Kyeong-Jin;Hong, Myung-Duk;Jo, Geun-Sik
    • Proceedings of the Korea Information Processing Society Conference
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    • 2015.10a
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    • pp.1281-1284
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    • 2015
  • Multi-day trip itinerary planning is complex and time consuming task, from selecting a list of worth visiting POIs to arranging them into an itinerary with various constraints and requirements. In this paper, we present CYTRIP, a multi-day trip itinerary planning system that engages human computation (i.e. crowd recommendation) to collaboratively recommend POIs by providing a shared workspace. CYTRIP takes input the collective intelligence of crowd (i.e. recommended POIs) to build a multi-day trip itinerary taking into account user's preferences, various time constraints and locations. Furthermore, we explain how we engage crowd in our system. The planning problem and domain are formulated as AI planning using PDDL3. The preliminary empirical experiments show that our domain formulation is applicable to both single-day and multi-day trip planning.

A Development of Optimal Travel Course Recommendation System based on Altered TSP and Elasticsearch Algorithm (변형된 TSP 및 엘라스틱서치 알고리즘 기반의 최적 여행지 코스 추천 시스템 개발)

  • Kim, Jun-Yeong;Jo, Kyeong-Ho;Park, Jun;Jung, Se-Hoon;Sim, Chun-Bo
    • Journal of Korea Multimedia Society
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    • v.22 no.9
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    • pp.1108-1121
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    • 2019
  • As the quality and level of life rise, many people are doing search for various pieces of information about tourism. In addition, users prefer the search methods reflecting individual opinions such as SNS and blogs to the official websites of tourist destination. Many of previous studies focused on a recommendation system for tourist courses based on the GPS information and past travel records of users, but such a system was not capable of recommending the latest tourist trends. This study thus set out to collect and analyze the latest SNS data to recommend tourist destination of high interest among users. It also aimed to propose an altered TSP algorithm to recommend the optimal routes to the recommended destination within an area and a system to recommend the optimal tourist courses by applying the Elasticsearch engine. The altered TSP algorithm proposed in the study used the location information of users instead of Dijkstra's algorithm technique used in previous studies to select a certain tourist destination and allowed users to check the recommended courses for the entire tourist destination within an area, thus offering more diverse tourist destination recommendations than previous studies.