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Shop-Type Recommendation Leveraging the Data from Social Media and Location-Based Services

机译:利用社交媒体和基于位置的服务中的数据的商店类型推荐

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摘要

It is an important yet challenging task for investors to determine the most suitable type of shop (e.g., restaurant, fashion) for a newly opened store. Traditional ways are predominantly field surveys and empirical estimation, which are not effective as they lack shop-related data. As social media and location-based services (LBS) are becoming more and more pervasive, user-generated data from these platforms are providing rich information not only about individual consumption experiences, but also about shop attributes. In this paper, we investigate the recommendation of shop types for a given location, by leveraging heterogeneous data that are mainly historical user preferences and location context from social media and LBS. Our goal is to select the most suitable shop type, seeking to maximize the number of customers served from a candidate set of types. We propose a novel bias learning matrix factorization method with feature fusion for shop popularity prediction. Features are defined and extracted from two perspectives: location, where features are closely related to location characteristics, and commercial, where features are about the relationships between shops in the neighborhood. Experimental results show that the proposed method outperforms state-of-the-art solutions.
机译:对于投资者来说,为新开设的商店确定最合适的商店类型(例如,餐厅,时装店)是一项重要而又具有挑战性的任务。传统方式主要是现场调查和经验估算,由于缺乏与商店相关的数据,因此无效。随着社交媒体和基于位置的服务(LBS)变得越来越普遍,来自这些平台的用户生成的数据不仅提供了有关个人消费体验以及商店属性的丰富信息。在本文中,我们通过利用异构数据来调查给定位置的商店类型的建议,这些数据主要是历史用户的偏好以及来自社交媒体和LBS的位置上下文。我们的目标是选择最合适的商店类型,以从一组候选类型中寻求最大的客户数量。我们提出了一种新颖的具有特征融合的偏差学习矩阵分解方法,用于店铺受欢迎程度预测。从两个角度定义和提取要素:位置与要素与位置特征密切相关的位置;以及商业,与周围商店之间的关系有关的商业。实验结果表明,所提出的方法优于最新的解决方案。

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