딥러닝 기반 스마트관광 추천 알고리즘 개발 연구: 새로운 OTA 레스토랑 추천 시스템의 제안
Deep Learning-based Smart Tourism Recommendation Algorithm Development Research: A Novel OTA Restaurant Recommender System
- 발행
- 2022 등록KCI API에는 발행일 항목이 없어 원문 등록일을 쓰고 있습니다. 실측으로 발행보다 최대 6년 늦습니다 — 재수집하면 발행연월로 바뀝니다.
- 소속·발행
- 경희대학교 스마트관광연구소
- 출처
- 국내 KCI
- 원문
- 원문 보기 ↗
개념
키워드
스마트관광, 추천 시스템, 관광객 의사결정, 딥러닝, 레스토랑 추천, 워드 임베딩, Smart Tourism, Recommender System, Tourist Decision Making, Deep Leaning, Restaurant Recommendation, Word Embedding
초록
The purpose of this study is to propose a smart tourism restaurant recommendation algorithm based on deep sequential interaction embedding to successfully predict the preferred choices of tourists and promote their decision-making. We use Doc2Vec, a deep learning-based natural language processing technology, to learn the representation vectors of tourists and items by considering their interactions’ sequence, and then input them into multi-layer perceptron and matrix factorization that make up a deep artificial neural network to learn nonlinearity and linearity of interactions. Finally, we predict the possibility of tourists preferring restaurants and recommend top-N restaurants for each user. Indeed, the results of comparing the TripAdvior and MovieLens data sets demonstrated that our proposed recommendation algorithm (DSESTRR) significantly improved the recommendation accuracy compared to other algorithms. Based on the above results, the usefulness of DSESTRR in the tourism field was discussed, and theoretical and practical implications were proposed.