A traditional cloud-to-edge recommender system can’t respond to user engagement and interests in real time. This article introduces on-device inference and on-device learning paradigms that can capture rich user behavior and respond to users’ changing interests in real time. The article also goes through system design choices and implementation details of different industrial applications that have served recommendations to billions of users, such as Kuaishou’s Short Video Recommendation on Mobile Devices, and Taobao’s (Alibaba) on-device recommender systems.
Two-tower model is widely adopted in industrial-scale retrieval and ranking workflows across a broad range of application domains, such as content recommendations, advertisement systems, and search engines. It is also the current go-to state-of-the-art solution for pre-ranking tasks. This article explores the history and current state of the Two Tower models and also highlights potential improvements proposed in some of the recently published literature. The goal here is to help understand what makes the Two Tower model an appropriate choice for a bunch of applications, and how it can be potentially extended from its current state.
Modern time series forecasting requires a model to learn from multiple related time series. These time series often number in thousands or millions. Traditional statistical models do not scale well to these settings because they learn individual series in isolation and do not share parameters across series. Various deep learning models have been proposed recently with different inductive biases to work effectively under these settings. This article explores some of the most popular advances in deep learning architectures for modern time series forecasting.
Statistical methods have been used in the time series domain for multiple decades. But given the recent advances in Machine Learning and especially its sub-domain Deep Learning, are statistical methods still superior for forecasting? In this article, we will do a deep dive into literature and recent time series competitions to do a multifaceted comparison between Statistical, Machine Learning, and Deep Learning methods for time series forecasting.
IntroductionLarge-scale information retrieval applications, such as recommender systems, search ranking, and text analysis often leverage feature interactions for effective modeling. These models are commonly deployed at the ranking stage of the cascade-style systems. In this article, I summarize the need for modeling feature interactions and introduce some of the most popular ML architectures designed around this theme. This article also highlights the high data sparsity issue, that makes it hard for ML algorithms to model second or higher-order feature interactions.
This article explains what explicit and implicit feedback data means for recommender systems. We discuss their characteristics and peculiarities concerning collaborative filtering based algorithms. Then we go over one of the most popular collaborative filtering algorithms for implicit data and implement it in Python with an example dataset.
