A Semantic Sequential Correlation Based LSTM Model for Next POI Recommendation

Zhan, Guanhua; Xu, Jian; Huang, Zhifeng; Zhang, Qiang; Xu, Ming; Zheng, Ning

doi:10.1109/mdm.2019.00-65

Cited by 9 publications

(4 citation statements)

References 19 publications

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“…Semantic correlations refer to the functional or thematic association between locations. Referring to extant literature [39,22], we calculate the semantic relationships between locations based on their categories. It has been shown that the higher the transition probability between two categories in the semantic sequences, the higher the semantic correction of the two categories [39].…”

Section: Framework Overviewmentioning

confidence: 99%

“…Referring to extant literature [39,22], we calculate the semantic relationships between locations based on their categories. It has been shown that the higher the transition probability between two categories in the semantic sequences, the higher the semantic correction of the two categories [39]. Therefore, we calculate the semantic relevance based on the number of cooccurrences between adjacent categoriesand then build the location semantic graph.…”

Section: Framework Overviewmentioning

confidence: 99%

“…Location semantic graph: To obtain the location semantic features, we first calculate the category correlation based on users' semantic trajectories τ c(U ) , and then build the location semantic graph G c . Specifically, for two categories (i.e., c 1 and c 2 ), if they are adjacent categories in u's semantic trajectory τ c(U ) , it means that there is a sequential semantic correlation between these two locations [48,39,41]. The strength of the correlation can be measured by the number of occurrences of the adjacency categories < c 1 − c 2 >.…”

Section: Algorithm 1 Location Semantic Correlation Calculationmentioning

confidence: 99%

“…-First, extant models focusing on location context, while capturing location influence [17,5] and spatio-temporal correlations [25,33], pay less attention to location semantics and semantic correlations among locations that reflect users' activity topics or preferences [39,48,3]. As shown in figure 1, the trajectory of Tom consists of "music bar -school -music hall -bookstore".…”

Section: Introductionmentioning

confidence: 99%

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Next location recommendation: a multi-context features integration perspective

Wei

Liu

et al. 2022

World Wide Web

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Next location recommendation aims to mine users' historical trajectories to predict their potentially preferred locations in the next moment. Although previous studies have explored the idea of incorporating location or social contextual information for recommendation, they still suffer from several major limitations: (1) not fully considering the semantic associations between locations, (2) not considering the heterogeneity in preferences of socially linked users, (3) not fully utilizing contextual information from distinctive sources to further improve the recommendation performance. In this paper, we propose a novel multi-context-based next location recommendation model that incorporates location context, trajectory context, and social context to obtain comprehensive users' preferences while allowing for interactions between contexts. Specifically, we first develop an efficient method combining both high-order location graphs and location semantic graphs to characterize

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Section: Framework Overviewmentioning

confidence: 99%