Multi-Metric AutoRec for High Dimensional and Sparse User Behavior Data Prediction

• URL: http://arxiv.org/abs/2212.13879v1
• Date: Tue, 20 Dec 2022 12:28:07 GMT
• Title: Multi-Metric AutoRec for High Dimensional and Sparse User Behavior Data Prediction
• Authors: Cheng Liang, Teng Huang, Yi He, Song Deng, Di Wu, Xin Luo
• Abstract summary: We propose a multi-metric AutoRec (MMA) based on the representative AutoRec. MMA enjoys the multi-metric orientation from a set of dispersed metric spaces, achieving a comprehensive representation of user data. MMA can outperform seven other state-of-the-art models in predicting unobserved user behavior data.
• Score: 10.351592131677018
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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