Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social Networks
As one of the important techniques to explore unknown places for users, the methods that are proposed for point-of-interest (POI) recommendation have been widely studied in recent years. Compared with traditional recommendation problems, POI recommendations are suffering from more challenges, such a...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/3574194 |
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| _version_ | 1832559376908419072 |
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| author | Lei Guo Haoran Jiang Xiyu Liu Changming Xing |
| author_facet | Lei Guo Haoran Jiang Xiyu Liu Changming Xing |
| author_sort | Lei Guo |
| collection | DOAJ |
| description | As one of the important techniques to explore unknown places for users, the methods that are proposed for point-of-interest (POI) recommendation have been widely studied in recent years. Compared with traditional recommendation problems, POI recommendations are suffering from more challenges, such as the cold-start and one-class 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-based embedding methods have shown its power in many recommendation tasks with its ability to extract high-level representations from raw data. According to the above observations, to well utilize the network information, a neural network-based 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-wise ranking-based method is then introduced. Finally, by regarding the pretrained network representations as the priors of the latent feature factors, an embedding-based 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-world datasets demonstrate the importance of the network embeddings and the effectiveness of our proposed method. |
| format | Article |
| id | doaj-art-d4d2583b72274032b4d2c7a8ab8ceb00 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-d4d2583b72274032b4d2c7a8ab8ceb002025-02-03T01:30:11ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/35741943574194Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social NetworksLei Guo0Haoran Jiang1Xiyu Liu2Changming Xing3Postdoctoral Research Station of Management Science and Engineering, Shandong Normal University, Jinan, ChinaShandong Post Company, Jinan, ChinaShandong Normal University, Jinan, ChinaShandong University of Finance and Economics, Jinan, ChinaAs one of the important techniques to explore unknown places for users, the methods that are proposed for point-of-interest (POI) recommendation have been widely studied in recent years. Compared with traditional recommendation problems, POI recommendations are suffering from more challenges, such as the cold-start and one-class 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-based embedding methods have shown its power in many recommendation tasks with its ability to extract high-level representations from raw data. According to the above observations, to well utilize the network information, a neural network-based 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-wise ranking-based method is then introduced. Finally, by regarding the pretrained network representations as the priors of the latent feature factors, an embedding-based 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-world datasets demonstrate the importance of the network embeddings and the effectiveness of our proposed method.http://dx.doi.org/10.1155/2019/3574194 |
| spellingShingle | Lei Guo Haoran Jiang Xiyu Liu Changming Xing Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social Networks Complexity |
| title | Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social Networks |
| title_full | Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social Networks |
| title_fullStr | Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social Networks |
| title_full_unstemmed | Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social Networks |
| title_short | Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social Networks |
| title_sort | network embedding aware point of interest recommendation in location based social networks |
| url | http://dx.doi.org/10.1155/2019/3574194 |
| work_keys_str_mv | AT leiguo networkembeddingawarepointofinterestrecommendationinlocationbasedsocialnetworks AT haoranjiang networkembeddingawarepointofinterestrecommendationinlocationbasedsocialnetworks AT xiyuliu networkembeddingawarepointofinterestrecommendationinlocationbasedsocialnetworks AT changmingxing networkembeddingawarepointofinterestrecommendationinlocationbasedsocialnetworks |