Meng Chen
Xiaohui Yu
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Abstract
Venue categories used in location-based social networks often exhibit a hierarchical structure, together with the category sequences derived from users’ check-ins. The two data modalities provide a wealth of information for us to capture the semantic relationships between those categories. To understand the venue semantics, existing methods usually embed venue categories into low-dimensional spaces by modeling the linear context (i.e., the positional neighbors of the given category) in check-in sequences. However, the hierarchical structure of venue categories, which inherently encodes the relationships between categories, is largely untapped. In this article, we propose a venue Category Embedding Model named Hier-CEM, which generates a latent representation for each venue category by embedding the Hierarchical structure of categories and utilizing multiple types of context. Specifically, we investigate two kinds of hierarchical context based on any given venue category hierarchy and show how to model them together with the linear context collaboratively. We apply Hier-CEM to three tasks on two real check-in datasets collected from Foursquare. Experimental results show that Hier-CEM is better at capturing both semantic and sequential information inherent in venues than state-of-the-art embedding methods.
- [1] . 2018. Personalized context-aware point of interest recommendation. ACM Trans. Inf. Syst. 36, 4 (2018), 1–28. Google Scholar
Digital Library
- [2] . 2018. A collaborative ranking model with multiple location-based similarities for venue suggestion. In Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval. 19–26. Google ScholarDigital Library
- [3] . 2018. Joint learning of hierarchical word embeddings from a corpus and a taxonomy. In Automated Knowledge Base Construction. http://www.akbc.ws/2019/.Google Scholar
- [4] . 2018. Content-aware hierarchical point-of-interest embedding model for successive POI recommendation. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 3301–3307. Google ScholarDigital Library
- [5] . 2019. MPE: A mobility pattern embedding model for predicting next locations. World Wide Web 22, 6 (2019), 2901–2920.Google ScholarCross Ref
- [6] . 2020. Modeling spatial trajectories with attribute representation learning. IEEE Trans. Knowl. Data Eng. (2020).Google ScholarCross Ref
- [7] . 2015. Vector space models of lexical meaning. The Handbook of Contemporary Semantic Theory. Wiley-Blackwell.Google ScholarCross Ref
- [8] . 2017. POI2Vec: Geographical latent representation for predicting future visitors. In Proceedings of the 31st AAAI Conference on Artificial Intelligence. Google ScholarDigital Library
- [9] . 2017. Category-aware next point-of-interest recommendation via listwise Bayesian personalized ranking. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. 1837–1843. Google ScholarDigital Library
- [10] . 2019. A joint context-aware embedding for trip recommendations. In Proceedings of the 35th International Conference on Data Engineering. IEEE, 292–303.Google ScholarCross Ref
- [11] . 2015. Entity hierarchy embedding. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics. 1292–1300.Google ScholarCross Ref
- [12] . 1993. Using statistical testing in the evaluation of retrieval experiments. In Proceedings of the SIGIR Conference on Research and Development in Information Retrieval. 329–338. Google ScholarDigital Library
- [13] . 2011. The semantics of similarity in geographic information retrieval. J. Spatial Inf. Sci. 2011, 2 (2011), 29–57.Google Scholar
- [14] . 2018. HST-LSTM: A hierarchical spatial-temporal long-short term memory network for location prediction. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 2341–2347. Google ScholarDigital Library
- [15] . 2014. Distributed representations of sentences and documents. In Proceedings of the 31st International Conference on Machine Learning. 1188–1196. Google ScholarDigital Library
- [16] . 2014. Dependency-based word embeddings. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. 302–308.Google ScholarCross Ref
- [17] . 2014. Neural word embedding as implicit matrix factorization. In Proceedings of the 28th Annual Conference on Neural Information Processing Systems. 2177–2185. Google ScholarDigital Library
- [18] . 2017. Investigating different syntactic context types and context representations for learning word embeddings. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2421–2431.Google ScholarCross Ref
- [19] . 2019. POI representation learning by a hybrid model. In Proceedings of the 20th IEEE International Conference on Mobile Data Management. IEEE, 485–490.Google ScholarCross Ref
- [20] . 2017. Context selection for embedding models. In Proceedings of the 31st Annual Conference on Neural Information Processing Systems. 4816–4825. Google ScholarDigital Library
- [21] . 2018. Semantic structure-based word embedding by incorporating concept convergence and word divergence. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence. Google ScholarDigital Library
- [22] . 2013. Personalized point-of-interest recommendation by mining users’ preference transition. In Proceedings of the 22nd ACM International Conference on Information & Knowledge Management. 733–738. Google ScholarDigital Library
- [23] . 2016. Exploring the context of locations for personalized location recommendations. In Proceedings of the 25th International Joint Conference on Artificial Intelligence. 1188–1194. Google ScholarDigital Library
- [24] . 2008. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, Nov. (2008), 2579–2605.Google Scholar
- [25] . 2018. A contextual attention recurrent architecture for context-aware venue recommendation. In Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 555–564. Google ScholarDigital Library
- [26] . 2013. Distributed representations of words and phrases and their compositionality. In Proceedings of the 27th Annual Conference on Neural Information Processing Systems. 3111–3119. Google ScholarDigital Library
- [27] . 2018. Webly supervised joint embedding for cross-modal image-text retrieval. In Proceedings of the 26th ACM International Conference on Multimedia. 1856–1864. Google ScholarDigital Library
- [28] . 2017. Interpretable probabilistic embeddings: Bridging the gap between topic models and neural networks. In Proceedings of the Conference on Artificial Intelligence and Natural Language. Springer, 167–180.Google Scholar
- [29] . 2019. Spatiotemporal representation learning for translation-based POI recommendation. ACM Trans. Inf. Syst. 37, 2 (2019), 1–24. Google ScholarDigital Library
- [30] . 2020. Heterogeneous graph-based joint representation learning for users and POIs in location-based social network. Inf. Process. Manag. 57, 2 (2020), 102151.Google ScholarDigital Library
- [31] . 2019. Category-aware location embedding for point-of-interest recommendation. In Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval. 173–176. Google ScholarDigital Library
- [32] . [n.d.]. Paired T-Test for Superiority by a Margin. Retrieved from: NCSS.com.Google Scholar
- [33] . 2017. LCE: A location category embedding model for predicting the category labels of POIs. In Proceedings of the International Conference on Neural Information Processing. Springer, 710–720.Google ScholarDigital Library
- [34] . 2018. Learning semantic structure-preserved embeddings for cross-modal retrieval. In Proceedings of the 26th ACM International Conference on Multimedia. 825–833. Google ScholarDigital Library
- [35] . 2016. Learning graph-based POI embedding for location-based recommendation. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. 15–24. Google ScholarDigital Library
- [36] . 2017. From ITDL to Place2Vec: Reasoning about place type similarity and relatedness by learning embeddings from augmented spatial contexts. In Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 35. Google ScholarDigital Library
- [37] . 2019. Revisiting user mobility and social relationships in LBSNs: A hypergraph embedding approach. In Proceedings of the World Wide Web Conference. 2147–2157. Google ScholarDigital Library
- [38] . 2015. NationTelescope: Monitoring and visualizing large-scale collective behavior in LBSNs. J. Netw. Comput. Applic. 55 (2015), 170–180.Google ScholarCross Ref
- [39] . 2016. Participatory cultural mapping based on collective behavior data in location-based social networks. ACM Trans. Intell. Syst. Technol. 7, 3 (2016), 30. Google ScholarDigital Library
- [40] . 2018. Unsupervised learning of parsimonious general-purpose embeddings for user and location modeling. ACM Trans. Inf. Syst. 36, 3 (2018), 32. Google ScholarDigital Library
- [41] . 2015. Joint modeling of users’ interests and mobility patterns for point-of-interest recommendation. In Proceedings of the 23rd ACM International Conference on Multimedia. 819–822. Google ScholarDigital Library
- [42] . 2019. Time-aware metric embedding with asymmetric projection for successive POI recommendation. World Wide Web 22, 5 (2019), 2209–2224. Google ScholarDigital Library
- [43] . 2020. A category-aware deep model for successive POI recommendation on sparse check-in data. In Proceedings of the Web Conference. 1264–1274. Google ScholarDigital Library
- [44] . 2016. Shorter-is-better: Venue category estimation from micro-video. In Proceedings of the 24th ACM International Conference on Multimedia. 1415–1424. Google ScholarDigital Library
- [45] . 2015. GeoSoCa: Exploiting geographical, social and categorical correlations for point-of-interest recommendations. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. 443–452. Google ScholarDigital Library
- [46] . 2017. Geo-Teaser: Geo-temporal sequential embedding rank for point-of-interest recommendation. In Proceedings of the 26th International Conference on World Wide Web. 153–162. Google ScholarDigital Library
- [47] . 2018. Joint representation learning for location-based social networks with multi-grained sequential contexts. ACM Trans. Knowl. Discov. Data 12, 2 (2018), 1–21. Google ScholarDigital Library
- [48] . 2018. A time-aware trajectory embedding model for next-location recommendation. Knowl. Inf. Syst. 56, 3 (2018), 559–579. Google ScholarDigital Library
- [49] . 2020. Hierarchy-aware global model for hierarchical text classification. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 1106–1117.Google ScholarCross Ref
- [50] . 2016. A general multi-context embedding model for mining human trajectory data. IEEE Trans. Knowl. Data Eng. 28, 8 (2016), 1945–1958.Google ScholarDigital Library
- [51] . 2018. DeepMove: Learning place representations through large scale movement data. In Proceedings of the IEEE International Conference on Big Data (Big Data). IEEE, 2403–2412.Google ScholarCross Ref
Index Terms
- Embedding Hierarchical Structures for Venue Category Representation
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