Learning from Multi-View Multi-Way Data via Structural Factorization Machines
This work addresses the challenge of integrating complementary multi-view data for predictive modeling, which is incremental in nature.
The paper tackles the problem of modeling multi-view multi-way data by introducing structural factorization machines (SFMs) that learn shared latent spaces and adjust view importance, resulting in improved prediction accuracy and computational efficiency compared to state-of-the-art methods.
Real-world relations among entities can often be observed and determined by different perspectives/views. For example, the decision made by a user on whether to adopt an item relies on multiple aspects such as the contextual information of the decision, the item's attributes, the user's profile and the reviews given by other users. Different views may exhibit multi-way interactions among entities and provide complementary information. In this paper, we introduce a multi-tensor-based approach that can preserve the underlying structure of multi-view data in a generic predictive model. Specifically, we propose structural factorization machines (SFMs) that learn the common latent spaces shared by multi-view tensors and automatically adjust the importance of each view in the predictive model. Furthermore, the complexity of SFMs is linear in the number of parameters, which make SFMs suitable to large-scale problems. Extensive experiments on real-world datasets demonstrate that the proposed SFMs outperform several state-of-the-art methods in terms of prediction accuracy and computational cost.