"When and Where?": Behavior Dominant Location Forecasting with Micro-blog Streams
This work addresses location forecasting for social media users, offering a significant performance gain but is incremental as it builds on existing approaches.
The paper tackles forecasting users' future locations from micro-blog streams by proposing a novel algorithm that exploits dynamic fluctuations in point-of-interest, achieving 62.10% mean average precision on unlabeled users and an 85.92% improvement over state-of-the-art methods.
The proliferation of smartphones and wearable devices has increased the availability of large amounts of geospatial streams to provide significant automated discovery of knowledge in pervasive environments, but most prominent information related to altering interests have not yet adequately capitalized. In this paper, we provide a novel algorithm to exploit the dynamic fluctuations in user's point-of-interest while forecasting the future place of visit with fine granularity. Our proposed algorithm is based on the dynamic formation of collective personality communities using different languages, opinions, geographical and temporal distributions for finding out optimized equivalent content. We performed extensive empirical experiments involving, real-time streams derived from 0.6 million stream tuples of micro-blog comprising 1945 social person fusion with graph algorithm and feed-forward neural network model as a predictive classification model. Lastly, The framework achieves 62.10% mean average precision on 1,20,000 embeddings on unlabeled users and surprisingly 85.92% increment on the state-of-the-art approach.