We explore the task of local music recommendation: provide listeners with personalized playlists of relevant tracks by artists who play most of their live events within a small geographic area. In this paper, we compare the performance of three standard recommender system algorithms (Item-Item Neighborhood (IIN), Alternating Least Squares for Implicit Feedback (ALS), and Bayesian Personalized Ranking (BPR)) on the task of local music recommendation using the Million Playlist Dataset. Despite the fact that techniques based on matrix factorization (ALS, BPR) typically perform best on large recommendation tasks, we find that the neighborhood-based approach (IIN) performs best for long-tail local music recommendation.

In modern recommender systems, both users and items are associated with rich side information, which can help understand users and items. Such information is typically heterogeneous and can be roughly categorized into flat and hierarchical side information. In this paper, we investigate the problem of exploiting heterogeneous side information for recommendations. Specifically, we propose a novel framework jointly captures flat and hierarchical side information with mathematical coherence. Empirical results show that our approach is able to lead a significant performance gain over the state-of-the-art methods.

Among the machine learning applications to business, recommender systems would take one of the top places when it comes to success and adoption. They help the user in accelerating the process of search while helping businesses maximize sales. Our paper addresses the gaps of providing a taxonomy of deep learning approaches to address recommender systems problems in the areas of cold start and candidate generation in recommender systems. We also summarize the advantages and limitations of these techniques while outlining areas for future research. 

Cross-Domain Collaborative Filtering (CDCF) provides a way to alleviate data sparsity and cold-start problems present in recommendation systems by exploiting the knowledge from related domains. In this work, we propose an end-to-end neural network model -- NeuCDCF, to address these challenges in a cross-domain setting. More importantly, NeuCDCF follows a wide and deep framework and it learns the representations combinedly from both matrix factorization and deep neural networks. We perform experiments on four real-world datasets and demonstrate that our model performs better than state-of-the-art CDCF models.

With the abundance of data in recent years, interesting challenges are posed in the area of recommender systems. Producing high quality recommendations with scalability and performance is the need of the hour. In this paper, we extend the SVD technique further for scalability and performance in the context of 1) multi-threading 2) multiple computational units (with the use of Graphical Processing Units) and 3) distributed computation. We used Compute Unified Device Architecture (CUDA) platform and related hardware for leveraging Graphical Processing Unit (GPU) along with block based SVD to demonstrate the advantages in terms of performance and memory.

Machine learning has recently enabled large advances in artificial intelligence, but these tend to be highly centralized. We propose a framework for participants to collaboratively build a dataset and use smart contracts to host a continuously updated model. This model will be shared publicly on a blockchain where it can be free to use for inference. Ideal learning problems include scenarios where a model is used many times for similar input such as personal assistants, playing games, recommender systems, etc. A free and open source implementation for the Ethereum blockchain is provided at https://github.com/microsoft/0xDeCA10B.

In academic literature, recommender systems are often evaluated on the task of next-item prediction. The procedure aims to give an answer to the question: "Given the natural sequence of user-item interactions up to time t, can we predict which item the user will interact with at time t+1?".  From a causal perspective, the system performs an intervention, and we want to measure its effect. Next-item prediction is often used as a fall-back objective when information about interventions and their effects (shown recommendations and whether they received a click) is unavailable. Through a series of simulated experiments with the RecoGym environment, we show where traditional offline evaluation schemes fall short.

Increased public interest in healthy lifestyles has motivated the study of algorithms that encourage people to follow a healthy diet. In this report we combine these two trends by developing a recommendation system to suggest ingredients that can be added to a partial recipe. We implement the item-based collaborative filtering algorithm using a high-dimensional, sparse dataset of recipes, which inherently contains only implicit feedback. We explore the effect of different similarity measures and dimensionality reduction on the quality of the recommendations, and find that our best method achieves a recall@10 of circa 40%.

Matrix Factorization (MF) is a common method for generating recommendations, where the proximity of entities like users or items in the embedded space indicates their similarity to one another. We describe a novel method to embed a hierarchy of related music entities in hyperbolic space. We also describe how a parametric empirical Bayes approach can be used to estimate link reliability between entities in the hierarchy. Applying these methods together to build personalized playlists for users in a digital music service yielded a large and statistically significant increase in performance during an A/B test, as compared to the Euclidean model.

Recommendation systems personalise suggestions to individuals to help them in their decision making and exploration tasks. In the ideal case, these recommendations, besides of being accurate, should also be novel and explainable. In this paper, to the best of our knowledge, we propose a new model, denoted as NEMF, that allows to trade-off the MF performance with respect to the criteria of novelty and explainability, while only minimally compromising on accuracy. In addition, we recommend a new explainability metric based on nDCG, which distinguishes a more explainable item from a less explainable item. An initial user study indicates how users perceive the different attributes of these "user" style explanations and our extensive experimental results demonstrate that we attain high accuracy by recommending also novel and explainable items.

Music recommender systems have become central parts of popular streaming platforms such as Last.fm, Pandora, or Spotify to help users find music that fits their preferences. THere, current algorithms typically employ collaborative filtering (CF) utilizing similarities between users' listening behaviors. Some approaches also combine CF with content features into hybrid recommender systems. While music recommender systems can provide quality recommendations to listeners of mainstream music artists, recent research has shown that they tend to discriminate listeners of unorthodox, low-mainstream artists. This is foremost due to the scarcity of usage data of low-mainstream music as music consumption patterns are biased towards popular artists.