User-generated social media data tagged with geographic information present messages of dynamic spatiotemporal trajectories. These increasing mobility data provide potential opportunities to enhance the understanding ...User-generated social media data tagged with geographic information present messages of dynamic spatiotemporal trajectories. These increasing mobility data provide potential opportunities to enhance the understanding of human mobility behaviors. Several trajectory data mining approaches have been proposed to benefit from these rich datasets, but fail to incorporate aspatial semantics in mining. This study investigates mining frequent moving sequences of geographic entities with transit time from geo-tagged data. Different from previous analysis of geographic feature only trajectories, this work focuses on extracting patterns with rich context semantics. We extend raw geographic trajectories generated from geo-tagged data with rich context semantic annotations, use regions-of-interest as stops to represent interesting places, enrich them with multiple aspatial semantic annotations, and propose a semantic trajectory pattern mining algorithm that returns basic and multidimensional semantic trajectory patterns. Experimental results demonstrate that semantic trajectory patterns from our method present semantically meaningful patterns and display richer semantic knowledge.展开更多
The rising prosperity of Location-based Social Networks(LBSNs)witnessed an explosion in the availability of geo-tagged social media data,which enables tremendous location-aware online services,especially next point of...The rising prosperity of Location-based Social Networks(LBSNs)witnessed an explosion in the availability of geo-tagged social media data,which enables tremendous location-aware online services,especially next point of interest(POI)recommendation.However,previous next POI recommendation studies usually adopt fix-length time windows for user check-in sequence modeling,leading to a limited capacity in capturing fine-grained user temporal preferences that easily change over time.Besides,existing methods often directly leverage multi-modal contexts as auxiliary to alleviate the data sparsity issue,which fails to fully exploit the sequential patterns of contextual information for inferring user interest drift.To address the above challenges,we propose a novel framework named iTourSPOT which extends traditional collaborative filtering methods with a context-aware POI embedding architecture.For enhancing temporal interests modeling capacity,we associate the context feature extraction with varying-length sessions and incorporate check-in frequencies of POIs as prior knowledge to instruct the session representation learning of our model.Moreover,a collaborative sequence transduction model is designed for joint context sequence modeling and session-based POI recommendation.Experimental results on a real-world geo-tagged photo dataset clearly demonstrate the effectiveness of the proposed framework when compared with state-of-the-art baseline methods,especially in both sparse and cold-start scenarios.展开更多
文摘User-generated social media data tagged with geographic information present messages of dynamic spatiotemporal trajectories. These increasing mobility data provide potential opportunities to enhance the understanding of human mobility behaviors. Several trajectory data mining approaches have been proposed to benefit from these rich datasets, but fail to incorporate aspatial semantics in mining. This study investigates mining frequent moving sequences of geographic entities with transit time from geo-tagged data. Different from previous analysis of geographic feature only trajectories, this work focuses on extracting patterns with rich context semantics. We extend raw geographic trajectories generated from geo-tagged data with rich context semantic annotations, use regions-of-interest as stops to represent interesting places, enrich them with multiple aspatial semantic annotations, and propose a semantic trajectory pattern mining algorithm that returns basic and multidimensional semantic trajectory patterns. Experimental results demonstrate that semantic trajectory patterns from our method present semantically meaningful patterns and display richer semantic knowledge.
基金supported by grants from the National Natural Science Foundation of China[grant numbers 41830645,41971331].
文摘The rising prosperity of Location-based Social Networks(LBSNs)witnessed an explosion in the availability of geo-tagged social media data,which enables tremendous location-aware online services,especially next point of interest(POI)recommendation.However,previous next POI recommendation studies usually adopt fix-length time windows for user check-in sequence modeling,leading to a limited capacity in capturing fine-grained user temporal preferences that easily change over time.Besides,existing methods often directly leverage multi-modal contexts as auxiliary to alleviate the data sparsity issue,which fails to fully exploit the sequential patterns of contextual information for inferring user interest drift.To address the above challenges,we propose a novel framework named iTourSPOT which extends traditional collaborative filtering methods with a context-aware POI embedding architecture.For enhancing temporal interests modeling capacity,we associate the context feature extraction with varying-length sessions and incorporate check-in frequencies of POIs as prior knowledge to instruct the session representation learning of our model.Moreover,a collaborative sequence transduction model is designed for joint context sequence modeling and session-based POI recommendation.Experimental results on a real-world geo-tagged photo dataset clearly demonstrate the effectiveness of the proposed framework when compared with state-of-the-art baseline methods,especially in both sparse and cold-start scenarios.
