Location-based social networks (LBSNs) add geographical information into traditional social networks and link people’s virtual and physical lives. As an important application of LBSNs, point-of-interest (POI) suggestion has become an important method to help users explore interesting and attractive locations in LBSNs. The main problems of POI suggestion include data sparsity and cold start, which have been paid much attention by existing techniques. There are two major challenges which can greatly influence the performance of suggestion accuracy. One is the fuzzy boundary between sentiments, i.e., the fine distinction between sentiments makes it difficult to classify words and texts after word-sentiment mapping operation. The other challenge is the unreliability of data quality represented by similarity metrics, which relies on data integrity and path reachability of a heterogeneous network. To cope with the above two challenges, we present a novel framework called C ommunity-based Sentiment E xtraction and N e t work E mbedding for POI R ecommendation (CENTER) for suggesting impressive POIs to a specific user in an effective fashion. The CENTER framework contains two essential techniques: (1) a latent probabilistic generative model called C ommunity-based S entiment E xtraction (CSE), which can accurately capture the sentiments from review content in LBSNs by taking into consideration the characteristics of social communities. The parameters of the CSE model can be inferred effectively by the Gibbs sampling method. The primary sentiments are obtained based on the distribution of sentiments; (2) a network embedding model called S entiment-aware N ework E mbedding for POI R ecommendation (SNER) is employed to learn the representation of the factors including POIs, users and textual sentiments in a low-dimensional embedding space. The joint training is utilized to alternatively sample all sets of edges in a heterogeneous information network. Extensive experiments were conducted on two large-scale real datasets, in order to evaluate the performance of the proposed CENTER framework. The results demonstrate that CENTER is superior to the state-of-the-art baseline methods in the effectiveness and efficiency of POI suggestion.

中文翻译：

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情感印象：通过嵌入异构 LBSN 的情感感知 POI 建议

基于位置的社交网络 (LBSN) 将地理信息添加到传统社交网络中，并将人们的虚拟生活和物理生活联系起来。作为 LBSN 的重要应用，兴趣点（POI）建议已成为帮助用户探索 LBSN 中有趣和有吸引力的位置的重要方法。POI建议的主要问题包括数据稀疏性和冷启动，这些问题已被现有技术广泛关注。有两个主要挑战会极大地影响建议准确性的表现。一是情感边界模糊，即情感之间的精细区分使得词-情感映射操作后难以对词和文本进行分类。另一个挑战是相似性度量所代表的数据质量的不可靠性，它依赖于异构网络的数据完整性和路径可达性。为了应对上述两个挑战，我们提出了一个新的框架，称为基于社区的情绪提取和氖 不工作POI嵌入推荐 (CENTER) 以有效的方式向特定用户推荐令人印象深刻的 POI。CENTER 框架包含两项基本技术：（1）潜在概率生成模型，称为基于社区情绪提取（CSE），通过考虑社交社区的特征，可以准确地从LBSN中的评论内容中捕获情感。CSE 模型的参数可以通过 Gibbs 抽样方法有效地推断出来。根据情绪分布得到初级情绪；(2) 称为网络嵌入模型情绪感知网络POI嵌入推荐 (SNER) 用于学习在低维嵌入空间中的 POI、用户和​​文本情感等因素的表示。联合训练用于对异构信息网络中的所有边集进行交替采样。在两个大规模真实数据集上进行了广泛的实验，以评估所提出的 CENTER 框架的性能。结果表明，CENTER 在 POI 建议的有效性和效率方面优于最先进的基线方法。