How to Retrain Recommender System? A Sequential Meta-Learning Method

Practical recommender systems need be periodically retrained to refresh the model with new interaction data. To pursue high model fidelity, it is usually desirable to retrain the model on both historical and new data, since it can account for both long-term and short-term user preference. However, a full model retraining could be very time-consuming and memory-costly, especially when the scale of historical data is large. In this work, we study the model retraining mechanism for recommender systems, a topic of high practical values but has been relatively little explored in the research community. Our first belief is that retraining the model on historical data is unnecessary, since the model has been trained on it before. Nevertheless, normal training on new data only may easily cause overfitting and forgetting issues, since the new data is of a smaller scale and contains fewer information on long-term user preference. To address this dilemma, we propose a new training method, aiming to abandon the historical data during retraining through learning to transfer the past training experience. Specifically, we design a neural network-based transfer component, which transforms the old model to a new model that is tailored for future recommendations. To learn the transfer component well, we optimize the "future performance" -- i.e., the recommendation accuracy evaluated in the next time period. Our Sequential Meta-Learning(SML) method offers a general training paradigm that is applicable to any differentiable model. We demonstrate SML on matrix factorization and conduct experiments on two real-world datasets. Empirical results show that SML not only achieves significant speed-up, but also outperforms the full model retraining in recommendation accuracy, validating the effectiveness of our proposals. We release our codes at: https://github.com/zyang1580/SML.Comment: Appear in SIGIR 202

Linear implicit approximations of invariant measures of semi-linear SDEs with non-globally Lipschitz coefficients

This article investigates the weak approximation towards the invariant measure of semi-linear stochastic differential equations (SDEs) under non-globally Lipschitz coefficients. For this purpose, we propose a linear-theta-projected Euler (LTPE) scheme, which also admits an invariant measure, to handle the potential influence of the linear stiffness. Under certain assumptions, both the SDE and the corresponding LTPE method are shown to converge exponentially to the underlying invariant measures, respectively. Moreover, with time-independent regularity estimates for the corresponding Kolmogorov equation, the weak error between the numerical invariant measure and the original one can be guaranteed with an order one. Numerical experiments are provided to verify our theoretical findings.Comment: 45 pages, 7 figure

Flow Dynamics of a Dodecane Jet in Oxygen Crossflow at Supercritical Pressures

In advanced aero-propulsion engines, kerosene is often injected into the combustor at supercritical pressures, where flow dynamics is distinct from the subcritical counterpart. Large-eddy simulation combined with real-fluid thermodynamics and transport theories of a N-dodecane jet in oxygen crossflow at supercritical pressures is presented. Liquid dodecane at 600 K is injected into a supercritical oxygen environment at 700 K at different supercritical pressures and jet-to-crossflow momentum flux ratios (J). Various vortical structures are discussed in detail. The results shown that, with the same jet-to-crossflow velocity ratio of 0.75, the upstream shear layer (USL) is absolutely unstable at 6.0 MPa (J = 7.1) and convectively unstable at 3.0 MPa (J = 13.2). This trend is consistent with the empirical criterion for the stability characteristics of a jet in crossflow at subcritical pressures (Jcr = 10). While decreasing J to 7.1 at 3.0 MPa, however, the dominant Strouhal number of the USL varies along the upstream jet trajectory, and the USL becomes convectively unstable. Such abnormal change in stability behavior can be attributed to the real-fluid effect induced by strong density stratification at pressure of 3.0 MPa, under which a point of inflection in the upstream mixing layer renders large density gradient and tends to stabilize the USL. The stability behavior with varying pressure and J is further corroborated by linear stability analysis. The analysis of spatial mixing deficiencies reveals that the mixing efficiency is enhanced at a higher jet-to-crossflow momentum flux ratio