Overview

Overview

Effective recommendation of items of interest to consumers has become critical for enterprises in domains such as retail, e-commerce, and online media. The focus on recommendation systems was energized by the Netflix competition, after which considerable efforts were made to build recommenders of high quality for a variety of application areas. Driven by the business successes, academic research in this field has also been active for many years. Though many scientific breakthroughs have been achieved, there are still tremendous challenges for pragmatically implementing and productizing scalable recommendation systems for real-world industrial applications. The increasing data sizes and system complexity adds further challenges to the deployment of recommender systems. Novel ideas are pressingly required for developing advanced recommender algorithms for high accuracy and business relevancy, constructing scalable and high-performance recommendation systems, applying cutting-edge machine learning technologies such as deep neural networks, evaluating recommendation systems with proper offline/online metrics under various contexts, etc. In general, it can be foreseen that recommendation system technology will continue to play a pivotal role in many industrial applications. A joint endeavor bringing together industry and academia is essential to further foster advances in this field.

The 1st Workshop on Scalable and Applicable Recommendation Systems therefore aims at providing a venue for discussion on the new trend and application of recommendation technologies. The objective of workshop is to benefit both academic research community and industrial practitioners with the state-of-the-arts in the realm.