学习地理偏好以推荐兴趣点

B. Liu, Yanjie Fu, Zijun Yao, Hui Xiong
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引用次数: 430

摘要

兴趣点(POI)推荐的问题是为移动用户提供个性化的兴趣点推荐,如餐馆。由于其复杂性及其与基于位置的社交网络(LBSNs)的联系,用户选择POI的决策过程是复杂的,并可能受到各种因素的影响,如用户偏好、地理影响和用户移动行为。虽然有一些关于POI建议的研究,但缺乏对多因素共同作用的综合分析。为此,本文提出了一种战略性地综合考虑各种因素的新型地理概率因子分析框架。具体来说,这个框架允许捕捉对用户签入行为的地理影响。此外,用户的移动性行为也可以在推荐模型中得到有效的利用。此外,推荐模型可以有效地利用用户签入计数数据作为隐含用户反馈来建模用户偏好。最后,在真实LBSNs数据上的实验结果表明,所提出的推荐方法以显著的裕度优于最先进的潜在因素模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning geographical preferences for point-of-interest recommendation
The problem of point of interest (POI) recommendation is to provide personalized recommendations of places of interests, such as restaurants, for mobile users. Due to its complexity and its connection to location based social networks (LBSNs), the decision process of a user choose a POI is complex and can be influenced by various factors, such as user preferences, geographical influences, and user mobility behaviors. While there are some studies on POI recommendations, it lacks of integrated analysis of the joint effect of multiple factors. To this end, in this paper, we propose a novel geographical probabilistic factor analysis framework which strategically takes various factors into consideration. Specifically, this framework allows to capture the geographical influences on a user's check-in behavior. Also, the user mobility behaviors can be effectively exploited in the recommendation model. Moreover, the recommendation model can effectively make use of user check-in count data as implicity user feedback for modeling user preferences. Finally, experimental results on real-world LBSNs data show that the proposed recommendation method outperforms state-of-the-art latent factor models with a significant margin.
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