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

## TL;DR

This paper introduces Structural Factorization Machines (SFMs), a multi-tensor approach that models multi-view, multi-way data, capturing shared structures and view importance, leading to improved prediction accuracy and scalability.

## Contribution

The paper proposes SFMs, a novel model that preserves multi-view data structure, learns shared latent spaces, and adjusts view importance, with linear complexity for large-scale applications.

## Key findings

- SFMs outperform state-of-the-art methods in accuracy
- SFMs have linear complexity, suitable for large datasets
- Experiments confirm effectiveness on real-world data

## Abstract

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.