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.

Search systems in online content platforms are typically biased toward a minority of highly consumed items, reflecting the most common user behavior of navigating toward content that is already familiar and popular. Query suggestions are a powerful tool to support query formulation and to encourage exploratory search and content discovery. However, classic approaches for query suggestions typically rely either on semantic similarity, which lacks diversity and does not reflect user searching behavior, or on a collaborative similarity measure mined from search logs, which suffers from data sparsity and is biased by highly popular queries. In this work, we argue that the task of query suggestion can be modelled as a link prediction task on a heterogeneous graph including queries and documents, enabling Graph Learning methods to effectively generate query suggestions encompassing both semantic and collaborative information. We perform an offline evaluation on an internal Spotify dataset of search logs and on two public datasets, showing that node2vec leads to an accurate and diversified set of results, especially on the large scale real-world data. We then describe the implementation in an instant search scenario and discuss a set of additional challenges tied to the specific production environment. Finally, we report the results of a large scale A/B test involving millions of users and prove that node2vec query suggestions lead to an increase in online metrics such as coverage (+1.42% shown search results pages with suggestions) and engagement (+1.21% clicks), with a specifically notable boost in the number of clicks on exploratory search queries (+9.37%).

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.