Personalized recommendation is the task of recommendation content to users based on their preferences and history. Providing personalized content is crucial for many emerging applications including health care, fitness, education, food, and entertainment. Today, accurate and efficient recommendation of items power many Internet services such as online search, marketing, e-commerce, and video streaming. In fact, recent estimates show that recommendation systems drive many Internet businesses. In 2018, estimates show that recommendation systems drove up-to 35% of Amazon’s revenue, 75% of movies watched on Netflix, and 60% of videos on Youtube. In addition, the fraction of cycles devoted to serving personalized recommendation models in Facebook’s datacenter -- recommendation accounts for 80% of all AI inference cycles.

While the machine learning and systems research community has devoted significant effort to optimize AI and in particular deep neural networks, the majority of work studies AI-enabled perception, speech recognition, and natural language processing. As a result, efforts across machine learning and systems researchers have primarily focused on convolutional neural networks (CNNs) and recurrent neural networks (RNNs). However, not all services use CNNs and RNNs. In fact, as deep learning forms the backbone of many Internet services, AI for personalized recommendation is arguably one of the most impactful, widely used, and understudied applications of DNNs.

In addition to their importance, modern deep learning solutions for personalized recommendation impose unique compute, memory access, and storage requirements compared to CNNs and RNNs. However, in 2019, less than 2% of research papers were devoted to optimizing systems for recommendation engines.

To address this underinvestment from the research community, we propose a venue to discuss, share, and foster research into personalized recommendation systems and algorithms.