DTN: Deep Multiple Task-specific Feature Interactions Network for Multi-Task Recommendation

Neural-based multi-task learning (MTL) has been successfully applied to many recommendation applications. However, these MTL models (e.g., MMoE, PLE) did not consider feature interaction during the optimization, which is crucial for capturing complex high-order features and has been widely used in ranking models for real-world recommender systems. Moreover, through feature importance analysis across various tasks in MTL, we have observed an interesting divergence phenomenon that the same feature can have significantly different importance across different tasks in MTL. To address these issues, we propose Deep Multiple Task-specific Feature Interactions Network (DTN) with a novel model structure design. DTN introduces multiple diversified task-specific feature interaction methods and task-sensitive network in MTL networks, enabling the model to learn task-specific diversified feature interaction representations, which improves the efficiency of joint representation learning in a general setup. We applied DTN to our company's real-world E-commerce recommendation dataset, which consisted of over 6.3 billion samples, the results demonstrated that DTN significantly outperformed state-of-the-art MTL models. Moreover, during online evaluation of DTN in a large-scale E-commerce recommender system, we observed a 3.28% in clicks, a 3.10% increase in orders and a 2.70% increase in GMV (Gross Merchandise Value) compared to the state-of-the-art MTL models. Finally, extensive offline experiments conducted on public benchmark datasets demonstrate that DTN can be applied to various scenarios beyond recommendations, enhancing the performance of ranking models.

A Fast Multitaper Power Spectrum Estimation in Nonuniformly Sampled Time Series

Nonuniformly sampled signals are prevalent in real-world applications but pose a significant challenge when estimating their power spectra from a finite number of samples of a single realization. The optimal solution using Bronez Generalized Prolate Spheroidal Sequence (GPSS) is computationally intensive and thus impractical for large datasets. This paper presents a fast nonparametric method, MultiTaper NonUniform Fast Fourier Transform (MTNUFFT), capable of estimating power spectra with lower computational burden. The method first derives a set of optimal tapers via cubic spline interpolation on a nominal analysis band, and subsequently shifts these tapers to other analysis bands using NonUniform FFT (NUFFT). The estimated spectral power within the band is the average power at the outputs of the taper set. This algorithm eliminates the time-consuming computation for solving the Generalized Eigenvalue Problem (GEP), thus reducing the computational load from $O(N^4)$ to $O(N \log N + N \log(1/\epsilon))$, comparable with the NUFFT. The statistical properties of the estimator are assessed using Bronez GPSS theory, revealing that the bias and variance bound of the MTNUFFT estimator are identical to those of the optimal estimator. Furthermore, the degradation of bias bound can serve as a measure of the deviation from optimality. The performance of the estimator is evaluated using both simulation and real-world data, demonstrating its practical applicability. The code of the proposed fast algorithm is available on GitHub (https://github.com/jiecui/mtnufft).