At Spotify, search has been traditionally seen as a tool for retrieving content, with the search system optimized for when the user has a specific target in mind. In particular we have relied on an instant search system providing results for each keystroke, which works well for known-item search, when queries are straightforward, and the catalog is small. However, as Spotify’s catalog grows in size and variety, it becomes increasingly difficult for users to define their search intents accurately. Furthermore, as we expand the offering, we need to help users discover more content both when it comes to new content types, e.g. audiobooks, as well as for new content/creators within existing content types. To solve this we have introduced a hybrid Query Recommendation system (QR) that helps the user formulate more complex exploratory search intents, while still serving known-item lookups efficiently. This experience has been rolled out worldwide to all mobile users resulting in an increase in exploratory intent queries of 9% in A/B tests.

Instant search systems present results to the user at every keystroke. This type of search system works best when the query ambiguity is low, the catalog is limited, and users know what they are looking for. However, Spotify's catalog is large and diverse, leading some users to struggle when formulating search intents. Query suggestions can be a powerful tool that helps users to express intents and explore content from the long-tail of the catalog. In this paper, we explain how we introduce query suggestions in Spotify's instant search system--a system that connects hundreds of millions of users with billions of items in our audio catalog. Specifically, we describe how we: (1) generate query suggestions from instant search logs, which largely contains in-complete prefix queries that cannot be directly applied as suggestions; (2) experiment with the generated suggestions in a specific UI feature, Related Searches; and (3) develop new metrics to measure whether the feature helps users to express search intent and formulate exploratory queries.

Online Fashion retailers have significantly increased in popularity over the last decade, making it possible for customers to explore hundreds of thousands of products without the need to visit multiple stores or stand in long queues for checkout. Recommender Systems are often used to solve different complex problems in this domain, such as social fashion-aware recommendations (outfits inspired by influencers), product recommendations, or size and fit recommendations. Moreover, the research interest on this area is increasing, demonstrated by the success of the past three editions of the fashionXrecsys Workshops 2019-21. The Fourth edition of the workshop aims at providing an avenue for continuing the discussion of novel approaches and applications of recommendation systems in fashion and e-commerce with a particular focus on pandemic era events and their short and long lasting effects on e-commerce and Fashion.

Online Fashion retailers have significantly increased in popularity over the last decade, making it possible for customers to explore hundreds of thousands of products without the need to visit multiple stores or stand in long queues for checkout. Recommender Systems are often used to solve different complex problems in this domain, such as social fashion-aware recommendations (outfits inspired by influencers), product recommendations, or size and fit recommendations. Moreover, the research interest on this area is increasing, demonstrated by the success of the first and second editions of the fashionXrecsys Workshop in 2019-2020. The third edition of the workshop aims at providing an avenue for continuing the discussion of novel approaches and applications of recommendation systems in fashion and e-commerce.

Online Fashion retailers have significantly increased in popularity over the last decade, making it possible for customers to explore hundreds of thousands of products without the need to visit multiple stores or stand in long queues for checkout. Recommender Systems are often used to solve different complex problems in this domain, such as social fashion-aware recommendations (outfits inspired by influencers), product recommendations, or size and fit recommendations. Moreover, the research interest on this area is increasing, demonstrated by the success of the first edition of the fashionXrecsys Workshop in 2019. The second edition of the workshop aims at providing an avenue for continuing the discussion of novel approaches and applications of recommendation systems in fashion and e-commerce.

Online Fashion retailers have significantly increased in popularity over the last decade, making it possible for customers to explore hundreds of thousands of products without the need to visit multiple stores or stand in long queues for checkout. Recommender Systems are often used to solve different complex problems in this scenario, such as social fashion-aware recommendations (outfits inspired by influencers), product recommendations, or size and fit recommendations. However, relatively little research has been done on these complex problems. The very First fashionXrecsys Workshop aims at addressing these issues by providing a avenue for discussing novel approaches to recommendations in fashion and e-commerce applications.