4 research outputs found
TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering
Recommender systems are a long-standing research problem in data mining and
machine learning. They are incremental in nature, as new user-item interaction
logs arrive. In real-world applications, we need to periodically train a
collaborative filtering algorithm to extract user/item embedding vectors and
therefore, a time-series of embedding vectors can be naturally defined. We
present a time-series forecasting-based upgrade kit (TimeKit), which works in
the following way: it i) first decides a base collaborative filtering
algorithm, ii) extracts user/item embedding vectors with the base algorithm
from user-item interaction logs incrementally, e.g., every month, iii) trains
our time-series forecasting model with the extracted time-series of embedding
vectors, and then iv) forecasts the future embedding vectors and recommend with
their dot-product scores owing to a recent breakthrough in processing
complicated time-series data, i.e., neural controlled differential equations
(NCDEs). Our experiments with four real-world benchmark datasets show that the
proposed time-series forecasting-based upgrade kit can significantly enhance
existing popular collaborative filtering algorithms.Comment: Accepted at IEEE BigData 202
EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting
Deep learning inspired by differential equations is a recent research trend
and has marked the state of the art performance for many machine learning
tasks. Among them, time-series modeling with neural controlled differential
equations (NCDEs) is considered as a breakthrough. In many cases, NCDE-based
models not only provide better accuracy than recurrent neural networks (RNNs)
but also make it possible to process irregular time-series. In this work, we
enhance NCDEs by redesigning their core part, i.e., generating a continuous
path from a discrete time-series input. NCDEs typically use interpolation
algorithms to convert discrete time-series samples to continuous paths.
However, we propose to i) generate another latent continuous path using an
encoder-decoder architecture, which corresponds to the interpolation process of
NCDEs, i.e., our neural network-based interpolation vs. the existing explicit
interpolation, and ii) exploit the generative characteristic of the decoder,
i.e., extrapolation beyond the time domain of original data if needed.
Therefore, our NCDE design can use both the interpolated and the extrapolated
information for downstream machine learning tasks. In our experiments with 5
real-world datasets and 12 baselines, our extrapolation and interpolation-based
NCDEs outperform existing baselines by non-trivial margins.Comment: main 8 page
Learnable Path in Neural Controlled Differential Equations
Neural controlled differential equations (NCDEs), which are continuous analogues to recurrent neural networks (RNNs), are a specialized model in (irregular) time-series processing. In comparison with similar models, e.g., neural ordinary differential equations (NODEs), the key distinctive characteristics of NCDEs are i) the adoption of the continuous path created by an interpolation algorithm from each raw discrete time-series sample and ii) the adoption of the Riemann--Stieltjes integral. It is the continuous path which makes NCDEs be analogues to continuous RNNs. However, NCDEs use existing interpolation algorithms to create the path, which is unclear whether they can create an optimal path. To this end, we present a method to generate another latent path (rather than relying on existing interpolation algorithms), which is identical to learning an appropriate interpolation method. We design an encoder-decoder module based on NCDEs and NODEs, and a special training method for it. Our method shows the best performance in both time-series classification and forecasting