Ten Challenges in Industrial Recommender Systems

TL;DR

This paper discusses ten significant challenges faced in industrial recommender systems, highlighting technological trends and opportunities based on Huawei's extensive experience in deploying large-scale recommendation solutions.

Contribution

It identifies and elaborates on ten key challenges in industrial recommender systems, providing insights to inspire future research and development in the field.

Findings

Identification of ten core challenges in industrial recommender systems

Analysis of technological trends from shallow to deep models

Insights into practical issues faced in large-scale deployment

Abstract

Huawei's vision and mission is to build a fully connected intelligent world. Since 2013, Huawei Noah's Ark Lab has helped many products build recommender systems and search engines for getting the right information to the right users. Every day, our recommender systems serve hundreds of millions of mobile phone users and recommend different kinds of content and services such as apps, news feeds, songs, videos, books, themes, and instant services. The big data and various scenarios provide us with great opportunities to develop advanced recommendation technologies. Furthermore, we have witnessed the technical trend of recommendation models in the past ten years, from the shallow and simple models like collaborative filtering, linear models, low rank models to deep and complex models like neural networks, pre-trained language models. Based on the mission, opportunities and technological trends, we have also met several hard problems in our recommender systems. In this talk, we will share ten important and interesting challenges and hope that the RecSys community can get inspired and create better recommender systems.