Yang Liu, Xingchen Ding, Maomao Chi, Jiang Wu, Lili Ma
{"title":"评估酒店评论对信息过载的帮助程度:一种多视角空间特征方法","authors":"Yang Liu, Xingchen Ding, Maomao Chi, Jiang Wu, Lili Ma","doi":"10.1007/s40558-023-00280-x","DOIUrl":null,"url":null,"abstract":"<p>Consumer perceptions of helpfulness remain an open question due to the lack of semantic and spatial features of review content. This paper aims to explore three aspects of the contents of a review: time, rating, and location, to assess the helpfulness of hotel reviews. A multi-view graph convolutional network (MVGCN) and attention mechanisms that capture multimodal semantic information are designed. The experimental results on Yelp and TripAdvisor are evaluated. The findings indicate that this facilitates the filtering of helpful information and avoids information overload when reading to customers. The results show that the proposed model outperforms the baseline and illustrates the interpretability of the models in each view. Our work is essential for professionals of both hotel and travel platforms that can utilize our findings to optimize their sales systems. Also, the results can help visitors or users acquire beneficial information and avoid information overload. This study is one of the few articles that can promote a model interpretable for information overload, which aims to guide research on evaluating the helpfulness of reviews in the hotel sector. This study contributes also to the methodology by developing extracting features of multimodal data, giving a multi-view feature with several novel assessments, and a novel framework involving deep learning.</p>","PeriodicalId":46275,"journal":{"name":"Information Technology & Tourism","volume":null,"pages":null},"PeriodicalIF":6.3000,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessing the helpfulness of hotel reviews for information overload: a multi-view spatial feature approach\",\"authors\":\"Yang Liu, Xingchen Ding, Maomao Chi, Jiang Wu, Lili Ma\",\"doi\":\"10.1007/s40558-023-00280-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Consumer perceptions of helpfulness remain an open question due to the lack of semantic and spatial features of review content. This paper aims to explore three aspects of the contents of a review: time, rating, and location, to assess the helpfulness of hotel reviews. A multi-view graph convolutional network (MVGCN) and attention mechanisms that capture multimodal semantic information are designed. The experimental results on Yelp and TripAdvisor are evaluated. The findings indicate that this facilitates the filtering of helpful information and avoids information overload when reading to customers. The results show that the proposed model outperforms the baseline and illustrates the interpretability of the models in each view. Our work is essential for professionals of both hotel and travel platforms that can utilize our findings to optimize their sales systems. Also, the results can help visitors or users acquire beneficial information and avoid information overload. This study is one of the few articles that can promote a model interpretable for information overload, which aims to guide research on evaluating the helpfulness of reviews in the hotel sector. This study contributes also to the methodology by developing extracting features of multimodal data, giving a multi-view feature with several novel assessments, and a novel framework involving deep learning.</p>\",\"PeriodicalId\":46275,\"journal\":{\"name\":\"Information Technology & Tourism\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.3000,\"publicationDate\":\"2023-12-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Technology & Tourism\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1007/s40558-023-00280-x\",\"RegionNum\":3,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"HOSPITALITY, LEISURE, SPORT & TOURISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Technology & Tourism","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1007/s40558-023-00280-x","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HOSPITALITY, LEISURE, SPORT & TOURISM","Score":null,"Total":0}
Assessing the helpfulness of hotel reviews for information overload: a multi-view spatial feature approach
Consumer perceptions of helpfulness remain an open question due to the lack of semantic and spatial features of review content. This paper aims to explore three aspects of the contents of a review: time, rating, and location, to assess the helpfulness of hotel reviews. A multi-view graph convolutional network (MVGCN) and attention mechanisms that capture multimodal semantic information are designed. The experimental results on Yelp and TripAdvisor are evaluated. The findings indicate that this facilitates the filtering of helpful information and avoids information overload when reading to customers. The results show that the proposed model outperforms the baseline and illustrates the interpretability of the models in each view. Our work is essential for professionals of both hotel and travel platforms that can utilize our findings to optimize their sales systems. Also, the results can help visitors or users acquire beneficial information and avoid information overload. This study is one of the few articles that can promote a model interpretable for information overload, which aims to guide research on evaluating the helpfulness of reviews in the hotel sector. This study contributes also to the methodology by developing extracting features of multimodal data, giving a multi-view feature with several novel assessments, and a novel framework involving deep learning.
期刊介绍:
Information Technology & Tourism stands as the pioneer interdisciplinary journal dedicated to exploring the essence and impact of digital technology in tourism, travel, and hospitality. It delves into challenges emerging at the crossroads of IT and the domains of tourism, travel, and hospitality, embracing perspectives from both technical and social sciences. The journal covers a broad spectrum of topics, including but not limited to the development, adoption, use, management, and governance of digital technology. It supports both theory-focused research and studies with direct relevance to the industry.