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arXiv · 2212.13879

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

Cheng Liang·Teng Huang·Yi He·Song Deng·Di Wu·Xin Luo
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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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Cheng Liang, Teng Huang, Yi He, Song Deng, Di Wu, Xin Luo. 2022-12-20. Multi-Metric AutoRec for High Dimensional and Sparse User Behavior Data Prediction. https://arxiv.org/abs/2212.13879

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