WWW’19 Tutorial on Explainable Recommendation and Search
Explainable recommendation and search attempt to develop models or methods that not only generate high-quality recommendation or search results, but also intuitive explanations of the results for users or system designers, where the explanations can be either post-hoc or directly come from an explainable model. Explainable recommendation and search can help to improve the system transparency, persuasiveness, trustworthiness, and effectiveness. This is even more important in personalized search and recommendation scenarios, where users would like to know why a particular product, web page, news report, or friend suggestion exists in his or her own search and recommendation lists. The tutorial focuses on the research and application of explainable recommendation and search algorithms, as well as their application in real-world systems such as search engine, e-commerce and social networks. The tutorial aims at introducing and communicating explainable recommendation and search methods to the community, as well as gathering researchers and practitioners interested in this research direction for discussions, idea communications, and research promotions.
Personalization in Practice: Methods and Applications
Personalization is one of the key applications in machine learning with widespread usage across e-commerce, entertainment, production, healthcare and many other industries. While various machine learning techniques present novel state-of-the-art advances and super-human performance year-over-year, personalization and recommender-systems applications are often late-adopters of novel solutions due to problem hardness and implementation complexity. This tutorial presents recent advances across the personalization industry and demonstrates their practical applications in real case-studies of world-leading online platforms. Key trends such as deep learning, causality and active exploration with bandits are depicted with real examples and demonstrated alongside their business considerations and implementation challenges.Rising topics like explainability, fairness, natural interfaces and content generation are covered, touching on aspects of both technology and user experience. Our tutorial relies on recent advances in the field and on work conducted at Booking.com, where we implement personalization models on one of the world's leading online travel platform.
Multi-layer inpainting on Chinese artwork
Digital inpainting uses spatial or frequency information to restore partially damaged/removed photos and artworks. Most restoration algorithms take a picture as a single layer. We propose a new approach, which subdivides a Chinese painting into several layers. Each layer is inpainted separately. The layer fusion mechanism then finds the optimal inpaint among layers, which are restored layer-bylayer. We apply the algorithm on Chinese and western drawing. The result shows a high PSNR value as well as a high user satisfaction. The demonstration of our work is available at: http://www.mine.tku.edu.tw/demos/inpaint.