Abstract
There is a growing trend recently in big data analysis that focuses on behavior interiors, which concern the semantic meanings (e.g., sentiment, controversy, and other state-dependent factors) in explaining the human behaviors from psychology, sociology, cognitive science, and so on, rather than the data per se as in the case of exterior dimensions. It is more intuitive and much easier to understand human behaviors with less redundancy in concept by exploring the behavior interior dimensions, compared with directly using behavior exteriors. However, they usually approach from a unidimensional perspective with a lack of a sense of interrelatedness. Thus, integrating multiple behavior dimensions together into some numerical measures to form a more comprehensive view for subsequent prediction processes becomes a pivotal issue. Moreover, these studies usually focus on the magnitude but neglect the associated temporal features. In this paper, we propose a behavior interior dimension-based neighborhood collaborative filtering method for the top-N hashtag adoption frequency prediction that takes into account the interdependence in temporal dynamics. Our proposed approach couples the similarity in user preference and their impact propagation, by integrating the linear threshold model and the enhanced CF model based on behavior interiors. Experiments on Twitter demonstrate that the behavior-interior-aware CF models achieve better adoption prediction results than the state-of-the-art methods, and the joint consideration of similarity in user preference and their impact propagation results in a significant improvement than treating them separately.
Highlights
Under big data era, dynamic behaviors of an entity, human, or object are often revealed through multiple interrelated data sources, each of which gives a “partial view” of the instantaneous behavior of the entity or the context that the entity is currently in
We propose a behavior interior dimension-based neighborhood collaborative filtering method for the top-N hashtag adoption frequency prediction that takes into account the interdependence in temporal dynamics
We summarize the contributions of our work as follows: (i) Firstly, we propose a behavior-interior-aware approach that captures the semantic meaning in the raw behavior traces instead of the exterior transactional features; the effectiveness of the proposed approach is verified empirically using big data of Twitter (ii) Secondly, we enhance the prediction accuracy in user-hashtag adoption by learning user preference through a behavior interior-based approach with the interdependence between multiple behavior interior dimensions and temporal relations both considered (iii) Thirdly, we offer a Jaccard index-based metric to gauge the difference in interior dimensions and exterior dimension-based approaches in learning users’ preferences to illustrate the effectiveness of the proposed approach (iv) Lastly, the explainability of hashtag recommendation models is greatly enhanced with the introduction of the behavior interiors
Summary
Dynamic behaviors of an entity, human, or object are often revealed through multiple interrelated data sources, each of which gives a “partial view” of the instantaneous behavior of the entity or the context that the entity is currently in. Traditional data mining approaches offer many solutions trying to discover the cooccurrence patterns among multiple data sources, but these solutions often do not emphasize the use of domain knowledge and semantics to uncover the causations behind From this perspective, we are motivated to categorize the dimensions (or features) used to characterize users/topics in two groups: interior dimensions and exterior dimensions. We propose a behavior interior dimension-based neighborhood collaborative filtering method for the top-N hashtag adoption frequency prediction Both the interdependence between multiple behavior interior dimensions and temporal relations are considered in learning user preference from their neighbors (i.e., with high similarity in behavior interior dimensions) to make future predictions.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
