Personalized Ranking Metric Embedding for Next New POI Recommendation
Personalized Ranking Metric Embedding for Next New POI Recommendation
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发表时间：
2015-07
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通讯作者：
Shanshan Feng;Xutao Li;Yi-feng Zeng;G. Cong;Yeow Meng Chee;Quan Yuan
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作者：
Shanshan Feng;Xutao Li;Yi-feng Zeng;G. Cong;Yeow Meng Chee;Quan Yuan
基于位置的社交网络（LBSN）的快速增长提供了大量的签到数据，这使得许多服务成为可能，例如，兴趣点（POI）推荐。在本文中，我们研究了下一个新的POI推荐问题，其中新的POI相对于用户的当前位置被推荐。挑战在于精确学习用户的序列信息和个性化推荐模型的困难。为此，我们采用度量嵌入方法进行推荐，避免了矩阵分解技术的缺点。我们提出了一个个性化的排名度量嵌入方法（PRME）来建模个性化的签入序列。我们进一步开发了一个PRME-G模型，它集成了顺序信息，个人偏好，和地理影响，以提高推荐性能。在两个真实世界的LBSN数据集上的实验表明，我们的新算法优于最先进的下一个POI推荐方法。
The rapidly growing of Location-based Social Networks (LBSNs) provides a vast amount of check-in data, which enables many services, e.g., point-of-interest (POI) recommendation. In this paper, we study the next new POI recommendation problem in which new POIs with respect to users' current location are to be recommended. The challenge lies in the difficulty in precisely learning users' sequential information and personalizing the recommendation model. To this end, we resort to the Metric Embedding method for the recommendation, which avoids drawbacks of the Matrix Factorization technique. We propose a personalized ranking metric embedding method (PRME) to model personalized check-in sequences. We further develop a PRME-G model, which integrates sequential information, individual preference, and geographical influence, to improve the recommendation performance. Experiments on two real-world LBSN datasets demonstrate that our new algorithm outperforms the state-of-the-art next POI recommendation methods.