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Compared with traditional recommendation problems, POI recommendations are suffering from more challenges, such as the cold\u2010start and one\u2010class collaborative filtering problems. Many existing studies have focused on how to overcome these challenges by exploiting different types of contexts (e.g., social and geographical information). However, most of these methods only model these contexts as regularization terms, and the deep information hidden in the network structure has not been fully exploited. On the other hand, neural network\u2010based embedding methods have shown its power in many recommendation tasks with its ability to extract high\u2010level representations from raw data. According to the above observations, to well utilize the network information, a neural network\u2010based embedding method (node2vec) is first exploited to learn the user and POI representations from a social network and a predefined location network, respectively. To deal with the implicit feedback, a pair\u2010wise ranking\u2010based method is then introduced. Finally, by regarding the pretrained network representations as the priors of the latent feature factors, an embedding\u2010based POI recommendation method is proposed. As this method consists of an embedding model and a collaborative filtering model, when the training data are absent, the predictions will mainly be generated by the extracted embeddings. In other cases, this method will learn the user and POI factors from these two components. Experiments on two real\u2010world datasets demonstrate the importance of the network embeddings and the effectiveness of our proposed method.<\/jats:p>","DOI":"10.1155\/2019\/3574194","type":"journal-article","created":{"date-parts":[[2019,11,4]],"date-time":"2019-11-04T23:50:51Z","timestamp":1572911451000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Network Embedding\u2010Aware Point\u2010of\u2010Interest Recommendation in Location\u2010Based Social Networks"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9408-7594","authenticated-orcid":false,"given":"Lei","family":"Guo","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1533-1887","authenticated-orcid":false,"given":"Haoran","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Xiyu","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Changming","family":"Xing","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2019,11,4]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"crossref","unstructured":"YeM. 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