A point of interest (POI) recommender system (RS) is one of the representative research areas based on the location-based social network (LBSN). Most POI RS studies utilized various implicit information or social information to improve recommendation accuracy. However, majority of these studies overlooked the importance of users’ initial check-in information. Users are affected by their first input data in online services, and this phenomenon is called the anchoring effect. In POI RSs, few studies have analyzed the association with the anchoring effect while other RS domains already verified this effect. In particular, a research area, including POI RS, that focuses on the importance of the initial input does not exist. In this paper, we propose a latent Dirichlet allocation (LDA) model based on the anchoring effect for POI RS. This model emphasizes the importance of initial check-in data and is called the anchor-LDA. Experimental results showed that the anchor-LDA outperformed existing LDA-based POI recommender algorithms. Furthermore, we validated the importance of initial check-in information on the LBSN.

兴趣点（POI）推荐系统（RS）是基于基于位置的社交网络（LBSN）的代表性研究领域之一。大多数POI RS研究利用各种隐式信息或社交信息来提高推荐准确性。但是，这些研究大多数都忽略了用户初始签到信息的重要性。用户会受到在线服务中第一个输入数据的影响，这种现象称为锚定效应。在POI RS中，很少有研究分析与锚定效应的关联，而其他RS域已经验证了这种效应。尤其是，没有一个研究领域（包括POI RS）专注于初始输入的重要性。在本文中，我们基于POI RS的锚定效应，提出了一种潜在的Dirichlet分配（LDA）模型。该模型强调初始签入数据的重要性，称为锚点LDA。实验结果表明，锚点LDA优于现有的基于LDA的POI推荐算法。此外，我们验证了LBSN上初始签到信息的重要性。