LCARS: a location-content-aware recommender system

Hongzhi Yin, Yizhou Sun, B. Cui, Zhiting Hu, Ling Chen
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引用次数: 354

Abstract

Newly emerging location-based and event-based social network services provide us with a new platform to understand users' preferences based on their activity history. A user can only visit a limited number of venues/events and most of them are within a limited distance range, so the user-item matrix is very sparse, which creates a big challenge for traditional collaborative filtering-based recommender systems. The problem becomes more challenging when people travel to a new city where they have no activity history. In this paper, we propose LCARS, a location-content-aware recommender system that offers a particular user a set of venues (e.g., restaurants) or events (e.g., concerts and exhibitions) by giving consideration to both personal interest and local preference. This recommender system can facilitate people's travel not only near the area in which they live, but also in a city that is new to them. Specifically, LCARS consists of two components: offline modeling and online recommendation. The offline modeling part, called LCA-LDA, is designed to learn the interest of each individual user and the local preference of each individual city by capturing item co-occurrence patterns and exploiting item contents. The online recommendation part automatically combines the learnt interest of the querying user and the local preference of the querying city to produce the top-k recommendations. To speed up this online process, a scalable query processing technique is developed by extending the classic Threshold Algorithm (TA). We evaluate the performance of our recommender system on two large-scale real data sets, DoubanEvent and Foursquare. The results show the superiority of LCARS in recommending spatial items for users, especially when traveling to new cities, in terms of both effectiveness and efficiency.
LCARS:一个位置内容感知推荐系统
新兴的基于位置和基于事件的社交网络服务为我们提供了一个新的平台,可以根据用户的活动历史了解他们的偏好。用户只能访问有限数量的场地/活动,并且大多数都在有限的距离范围内,因此用户-物品矩阵非常稀疏,这对传统的基于协同过滤的推荐系统构成了很大的挑战。当人们前往一个没有活动历史的新城市时,这个问题变得更具挑战性。在本文中,我们提出了LCARS,这是一个位置内容感知推荐系统,通过考虑个人兴趣和当地偏好,为特定用户提供一组场地(例如,餐馆)或活动(例如,音乐会和展览)。这个推荐系统不仅可以方便人们在他们居住的地区附近旅行,还可以方便人们在他们陌生的城市旅行。具体来说,LCARS由离线建模和在线推荐两部分组成。离线建模部分称为LCA-LDA,旨在通过捕获项目共现模式和利用项目内容来学习每个用户的兴趣和每个城市的本地偏好。在线推荐部分自动结合查询用户的学习兴趣和查询城市的本地偏好,生成top-k推荐。为了加快在线查询处理的速度,通过扩展经典的阈值算法(TA),开发了一种可扩展的查询处理技术。我们在豆瓣事件和Foursquare两个大规模真实数据集上评估了我们的推荐系统的性能。结果表明,LCARS在为用户推荐空间项目方面具有优势,特别是在前往新城市旅行时,在有效性和效率方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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