Multi-Metric AutoRec for High Dimensional and Sparse User Behavior Data Prediction
This work addresses the challenge of improving recommendation systems for handling sparse user data, though it appears incremental as it builds on the existing AutoRec framework.
The paper tackles the problem of predicting user behavior from heterogeneous and sparse data by proposing a multi-metric AutoRec model that aggregates variant models with different loss functions and regularizations, achieving performance improvements over seven state-of-the-art models on five real-world datasets.
User behavior data produced during interaction with massive items in the significant data era are generally heterogeneous and sparse, leaving the recommender system (RS) a large diversity of underlying patterns to excavate. Deep neural network-based models have reached the state-of-the-art benchmark of the RS owing to their fitting capabilities. However, prior works mainly focus on designing an intricate architecture with fixed loss function and regulation. These single-metric models provide limited performance when facing heterogeneous and sparse user behavior data. Motivated by this finding, we propose a multi-metric AutoRec (MMA) based on the representative AutoRec. The idea of the proposed MMA is mainly two-fold: 1) apply different $L_p$-norm on loss function and regularization to form different variant models in different metric spaces, and 2) aggregate these variant models. Thus, the proposed MMA enjoys the multi-metric orientation from a set of dispersed metric spaces, achieving a comprehensive representation of user data. Theoretical studies proved that the proposed MMA could attain performance improvement. The extensive experiment on five real-world datasets proves that MMA can outperform seven other state-of-the-art models in predicting unobserved user behavior data.