本文除了像传统POI推荐那样，考虑POI之间的前后次序信息（Geographical influence），还考虑了POI的文本信息，这个文本信息来自于用户对其访问过的POI的描述信息。本文把这些描述信息用单词list的形式刻画出来。这样单词之间就有了同POI一样的序的关系。这样就可以利用skip-gram的思想（给定一个POI，那么他前面出现的POI和后面出现的POI应该同这个POI很近，表现为条件概率）学习出POI embedding和word embedding。
学习得到的POI embedding和word embedding，可以进一步用在LSTM等需要init item embedding的模型当中，这也是文中说的，其他方法在使用LSTM等模型时，vector 多是随机初始化的，而本文利用POI2Vec的方法学出的embedding可以最为其他方法的init vectors。（或许有其他的方法也可以结合POI2Vec和LFM）
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Content-Aware Hierarchical Point-of-Interest Embedding Model for Successive POI Recommendation | |||||||||||
| 文献作者 | Buru Chang | ||||||||||
| 文献发表年限 | 2018 | ||||||||||
| 文献关键字 | |||||||||||
| embedding learning; word2vec; content based; POI; skip-gram； 初始化隐向量 | |||||||||||
| 摘要描述 | |||||||||||
| Recommending a point-of-interest (POI) a user will visit next based on temporal and spatial context information is an important task in mobile-based applications. Recently, several POI recommendation models based on conventional sequential-data modeling approaches have been proposed. However, such models focus on only a user’s check-in sequence information and the physical distance between POIs. Furthermore, they do not utilize the characteristics of POIs or the relationships between POIs. To address this problem, we propose CAPE, the first content-aware POI embedding model which utilizes text content that provides information about the characteristics of a POI. CAPE consists of a check-in context layer and a text content layer. The check-in context layer captures the geographical influence of POIs from the check-in sequence of a user, while the text content layer captures the characteristics of POIs from the text content. To validate the efficacy of CAPE, we constructed a large-scale POI dataset. In the experimental evaluation, we show that the performance of the existing POI recommendation models can be significantly improved by simply applying CAPE to the models. | |||||||||||
