Relevance Models for Multi-Contextual Appropriateness in Point-of-Interest Recommendation

Anirban Chakraborty, Debasis Ganguly, Owen Conlan
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引用次数: 10

Abstract

Trip-qualifiers, such as 'trip-type' (vacation, work etc.), 'accompanied-by' (e.g., solo, friends, family etc.) are potentially useful sources of information that could be used to improve the effectiveness of POI recommendation in a current context (with a given set of these constraints). Using such information is not straight forward because a user's text reviews about the POIs visited in the past do not explicitly contain such annotations (e.g., a positive review about a pub visit does not contain the information on whether the user was with friends or alone, on a business trip or vacation). We propose to use a small set of manually compiled knowledge resource to predict the associations between the review texts in a user profile and the likely trip contexts. We demonstrate that incorporating this information within an IR-based relevance modeling framework significantly improves POI recommendation.
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兴趣点推荐中多上下文适当性的关联模型
旅行限定词,如“旅行类型”(度假、工作等)、“陪同”(例如,独自一人、朋友、家人等)是潜在的有用信息来源,可用于提高POI推荐在当前上下文(具有给定的这些约束集合)中的有效性。使用这样的信息并不是直截了当的,因为用户关于过去访问过的poi的文本评论并没有明确地包含这样的注释(例如,关于一次酒吧访问的正面评论不包含关于用户是与朋友一起还是独自一人、出差还是度假的信息)。我们建议使用一小部分手工编译的知识资源来预测用户简介中的评论文本与可能的旅行上下文之间的关联。我们证明,将这些信息合并到基于ir的相关性建模框架中可以显著提高POI推荐。
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