1,387 research outputs found
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-cyanoÂbenzylÂidene)isonicotinoÂhydrazide]silver(I) trifluoroÂacetate
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-cyanoÂbenzylÂidene)isonicotinohydrazide ligands in a nearly linear geometry. In the crystal structure, a combination of close contacts formed via Ag⋯N interÂactions [Ag⋯N = 3.098 (2) and 3.261 (2) Å] from symmetry-related molÂecules and interÂmolecular N—H⋯O hydrogen bonds between CF3CO2
− anions and the hydrazone groups of two ligands give rise to chains. Furthermore, there are Ag⋯O interÂactions 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)
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