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%).

We present Probabilistic Orthogonal Matching Pursuit (PrOMP), a novel probabilistic approach that builds upon orthogonal matching pursuit (OMP) for sparse representations of data. Like OMP, PrOMP is a greedy algorithm for regression that iteratively selects columns of a matrix according to a score. This score is based on a rarely employed feature of the EM algorithm and thus optimizes a marginal probability distribution. While OMP uses correlation as the score, in our probabilistic approach we define the scores to be the value of the resulting marginal likelihood—if adding a new signal does not improve this term, the algorithm automatically terminates. Our theoretical analysis also builds on the previous theory for OMP. We demonstrate the algorithm with a focus on a sparse dictionary learning and signal representation task using Bayesian nonparametrics. We first consider the nonparametric Beta Process Factor Analysis (BPFA) model. In addition, we present a new model based on BPFA that we call Beta Process Subspace Analysis (BPSA) which learns a set of subspaces and their respective dimensionalities from data.

Recommender systems play a key role in helping users find their favorite music to play among an often extremely large catalog of items on online streaming services. To correctly identify users' interests, recommendation algorithms rely on past user behavior and feedback to aim at learning users' preferences through the logged interactions. User modeling is a fundamental part of this large-scale system as it enables the model to learn an optimal representation for each user. For instance, in music recommendation, the focus of this paper, users' interests at any time is shaped by their general preferences for music as well as their recent or momentary interests in a particular type of music. In this paper, we present a novel approach for learning user representation based on general and slow-changing user interests as well as fast-moving current preferences. We propose a variational autoencoder-based model that takes fast and slow-moving features and learns an optimal user representation. Our model, which we call FS-VAE, consists of sequential and non-sequential encoders to capture patterns in user-item interactions and learn users' representations. We evaluate FS-VAE on a real-world music streaming dataset. Our experimental results show a clear improvement in learning optimal representations compared to state-of-the-art baselines on the next item recommendation task. We also demonstrate how each of the model components, slow input feature, and fast ones play a role in achieving the best results in next item prediction and learning users' representations.

Recommender systems are modulating what billions of people are exposed to on a daily basis. Typically, these systems are optimized for user engagement signals such as clicks, streams, likes, or a weighted combination of such sets. Despite the pervasiveness of this practice, little research has been done to explore the downstream impacts of optimization choice on users, creators and the ecosystem they are offered in. We used a platform that caters recommendations to millions of people and show in practice what you optimize for can have a large impact on the content users are exposed to, as well as what they end up consuming.