Learning from Multi-View Multi-Way Data via Structural Factorization Machines

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.Comment: 10 page

Bis[N′-(3-cyano­benzyl­idene)isonicotino­hydrazide]silver(I) trifluoro­acetate

In the title compound, [Ag(C14H10N4O)2]CF3CO2, the AgI ion is coordinated by two N atoms of the pyridine rings of two N′-(3-cyano­benzyl­idene)isonicotinohydrazide ligands in a nearly linear geometry. In the crystal structure, a combination of close contacts formed via Ag⋯N inter­actions [Ag⋯N = 3.098 (2) and 3.261 (2) Å] from symmetry-related mol­ecules and inter­molecular N—H⋯O hydrogen bonds between CF3CO2 − anions and the hydrazone groups of two ligands give rise to chains. Furthermore, there are Ag⋯O inter­actions with a separation of 2.765 (2) Å between chains. The F atoms of the CF3CO2 − anion are disordered over two sites with refined occupancies of 0.593 (5) and 0.407 (5)