An ensemble approach of recurrent neural networks using pre-trained embeddings for playlist completion

This paper describes the approach of the D2KLab team to the RecSys Challenge 2018 that focuses on the task of playlist completion. We propose an ensemble strategy of different recurrent neural networks leveraging pre-trained embeddings representing tracks, artists, albums, and titles as inputs. We also use lyrics from which we extract semantic and stylistic features that we fed into the network for the creative track. The RNN learns a probabilistic model from the sequences of items in the playlist, which is then used to predict the most likely tracks to be added to the playlist. Concerning the playlists without tracks, we implemented a fall-back strategy called Title2Rec that generates recommendations using only the playlist title. We optimized the RNN, Title2Rec, and the ensemble approach on a validation set, tuning hyper-parameters such as the optimizer algorithm, the learning rate, and the generation strategy. This approach is effective in predicting tracks for a playlist and flexible to include diverse types of inputs, but it is also computationally demanding in the training phase

Music recommender systems. Proof of concept

Data overload is a well-known problem due to the availability of big on-line distributed databases. While providing a wealth of information the difficulties to find the sought data and the necessary time spent in the search call for technological solutions. Classical search engines alleviate this problem and at the same time have transformed the way people access to the information they are interested in. On the other hand, Internet also has changed the music consuming habits around the world. It is possible to find almost every recorded song or music piece. Over the last years music streaming platforms like Spotify, Apple Music or Amazon Music have contributed to a substantial change of users’ listening habits and the way music is commercialized and distributed. On-demand music platforms offer their users a huge catalogue so they can do a quick search and listen what they want or build up their personal library. In this context Music Recommender Systems may help users to discover music that match their tastes. Therefore music recommender systems are a powerful tool to make the most of an immense catalogue, impossible to be fully known by a human. This project aims at testing different music recommendation approaches applied to the particular case of users playlists. Several recommender alternatives were designed and evaluated: collaborative filtering systems, content-based systems and hybrid recommender systems that combine both techniques. Two systems are proposed. One system is content-based and uses correlation between tracks characterized by high-level descriptors and the other is an hybrid recommender that first apply a collaborative method to filter the database and then computes the final recommendation using Gaussian Mixture Models. Recommendations were evaluated using objective metrics and human evaluations, obtaining positive results.Ingeniería de Sistemas Audiovisuale