Zhiyong Yu

TL;DR: A STAP model is proposed that first models the spatial and temporal activity preference separately, and then uses a principle way to combine them for preference inference, and a context-aware fusion framework is put forward to combine the temporal and spatial activity preference models for preferences inference.

TL;DR: This research proposes a hybrid user location preference model by combining the preference extracted from check-ins and text-based tips which is processed using sentiment analysis techniques and develops a location based social matrix factorization algorithm that takes both user social influence and venue similarity influence into account in location recommendation.

TL;DR: A novel multimode multi-attribute edge-centric coclustering framework to discover the overlapping and hierarchical communities of LBSNs users, not only able to group like-minded users from different social perspectives but also discover communities with explicit profiles indicating the interests of community members.

TL;DR: This paper first collects user check-ins and tips from Foursquare and uses them as direct user feedback on locations, and extracts users' sentiment about locations and associated entities from tips to characterize their fine-grained location preference, which is incorporated into personalized location ranking using tensor factorization techniques.

TL;DR: This paper has proposed a new data prediction method multi-node multi-feature (MNMF) based on bidirectional long short-term memory (LSTM) network that has better performance compared with the other methods in many evaluation indicators.