Related papers: Towards Effective Exploration/Exploitation in Sequ…

Machine Learning models are being utilized extensively to drive recommender systems, which is a widely explored topic today. This is especially true of the music industry, where we are witnessing a surge in growth. Besides a large chunk of…

Recommender systems have been widely used for various scenarios, such as e-commerce, news, and music, providing online contents to help and enrich users' daily life. Different scenarios hold distinct and unique characteristics, calling for…

At the core of many important machine learning problems faced by online streaming services is a need to model how users interact with the content they are served. Unfortunately, there are no public datasets currently available that enable…

We propose and design recommendation systems that incentivize efficient exploration. Agents arrive sequentially, choose actions and receive rewards, drawn from fixed but unknown action-specific distributions. The recommendation system…

In recent years, streaming music platforms have become very popular mainly due to the huge number of songs these systems make available to users. This enormous availability means that recommendation mechanisms that help users to select the…

Most modern recommendation algorithms are data-driven: they generate personalized recommendations by observing users' past behaviors. A common assumption in recommendation is that how a user interacts with a piece of content (e.g., whether…

The traditional recommendation framework seeks to connect user and content, by finding the best match possible based on users past interaction. However, a good content recommendation is not necessarily similar to what the user has chosen in…

Exploration is essential to improve long-term recommendation quality, but it often degrades short-term business performance, especially in remote-first TV environments where users engage passively, expect instant relevance, and offer few…

Streaming services use recommender systems to surface the right music to users. Playlists are a popular way to present music in a list-like fashion, ie as a plain list of songs. An alternative are tours, where the songs alternate segues,…

Session length is a very important aspect in determining a user's satisfaction with a media streaming service. Being able to predict how long a session will last can be of great use for various downstream tasks, such as recommendations and…

The task of a music recommender system is to predict what music item a particular user would like to listen to next. This position paper discusses the main challenges of the music preference prediction task: the lack of information on the…

Sequential recommendation aims to choose the most suitable items for a user at a specific timestamp given historical behaviors. Existing methods usually model the user behavior sequence based on the transition-based methods like Markov…

In this work, we investigate the personalization of text-to-music diffusion models in a few-shot setting. Motivated by recent advances in the computer vision domain, we are the first to explore the combination of pre-trained text-to-audio…

While both the data volume and heterogeneity of the digital music content is huge, it has become increasingly important and convenient to build a recommendation or search system to facilitate surfacing these content to the user or consumer…

Music Recommender Systems (mRS) are designed to give personalised and meaningful recommendations of items (i.e. songs, playlists or artists) to a user base, thereby reflecting and further complementing individual users' specific music…

Recommender systems shape music listening worldwide due to their widespread adoption on online platforms. Growing concerns about representational harms that these systems may cause are increasingly part of the scientific and public debate,…

Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listening profiles. Previous work has already identified several…

Music similarity is an essential aspect of music retrieval, recommendation systems, and music analysis. Moreover, similarity is of vital interest for music experts, as it allows studying analogies and influences among composers and…

Billions of USD are invested in new artists and songs by the music industry every year. This research provides a new strategy for assessing the hit potential of songs, which can help record companies support their investment decisions. A…

Being able to automatically and quickly understand the user context during a session is a main issue for recommender systems. As a first step toward achieving that goal, we propose a model that observes in real time the diversity brought by…