Neural Attentive Multiview Machines
This addresses the challenge of multiview representation learning for recommendation systems, offering a robust solution for datasets with missing views, though it appears incremental as it builds on existing attention mechanisms.
The paper tackles the problem of optimally combining multiple views for representation learning in recommendation tasks, introducing NAM, a Neural Attentive Multiview machine that uses attention to quantify view relevancy and handle missing views, and demonstrates it outperforms single-view and alternative multiview methods in movie and app recommendations, including cold-start scenarios.
An important problem in multiview representation learning is finding the optimal combination of views with respect to the specific task at hand. To this end, we introduce NAM: a Neural Attentive Multiview machine that learns multiview item representations and similarity by employing a novel attention mechanism. NAM harnesses multiple information sources and automatically quantifies their relevancy with respect to a supervised task. Finally, a very practical advantage of NAM is its robustness to the case of dataset with missing views. We demonstrate the effectiveness of NAM for the task of movies and app recommendations. Our evaluations indicate that NAM outperforms single view models as well as alternative multiview methods on item recommendations tasks, including cold-start scenarios.