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Demographics, Weather and Online Reviews: A Study of Restaurant Recommendations

机译:受众特征,天气和在线评论:餐厅推荐研究

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Online recommendation sites are valuable information sources that people contribute to, and often use to choose restaurants. However, little is known about the dynamics behind participation in these online communities and how the recommendations in these communities are formed. In this work, we take a first look at online restaurant recommendation communities to study what endogenous (i.e., related to entities being reviewed) and exogenous factors influence people's participation in the communities, and to what extent. We analyze an online community corpus of 840K restaurants and their 1.1M associated reviews from 2002 to 2011, spread across every U.S. state. We construct models for number of reviews and ratings by community members, based on several dimensions of endogenous and exogenous factors. We find that while endogenous factors such as restaurant attributes (e.g., meal, price, service) affect recommendations, surprisingly, exogenous factors such as demographics (e.g., neighborhood diversity, education) and weather (e.g., temperature, rain, snow, season) also exert a significant effect on reviews. We find that many of the effects in online communities can be explained using offline theories from experimental psychology. Our study is the first to look at exogenous factors and how it related to online online restaurant reviews. It has implications for designing online recommendation sites, and in general, social media and online communities.
机译:在线推荐站点是人们贡献的有价值的信息来源,通常用于选择餐馆。但是,对于参与这些在线社区背后的动态以及这些社区中的建议的形成方式知之甚少。在这项工作中,我们首先查看了在线餐厅推荐社区,以研究哪些内生因素(即与正在审查的实体相关)和外在因素影响人们在社区中的参与程度。我们分析了2002年至2011年间840,000家餐厅的在线社区语料库及其相关的110万条评论,这些评论遍及美国各州。我们根据内源性和外源性因素的多个维度,构建社区成员的评论和评级数量模型。我们发现,虽然内在因素(例如餐厅属性(例如,餐食,价格,服务))会影响推荐,但令人惊讶的是,外在因素(例如人口统计(例如,邻里多样性,教育程度)和天气(例如,温度,雨,雪,季节)也对评论产生重大影响。我们发现,可以使用来自实验心理学的离线理论来解释在线社区中的许多影响。我们的研究是第一个研究外在因素及其与在线在线餐厅评论的关系的研究。它对设计在线推荐站点以及一般的社交媒体和在线社区具有影响。

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