Accuracy-diversity trade-off in recommender systems via graph convolutions

Graph convolutions, in both their linear and neural network forms, have reached state-of-the-art accuracy on recommender system (RecSys) benchmarks. However, recommendation accuracy is tied with diversity in a delicate trade-off and the potential of graph convolutions to improve the latter is unexplored. Here, we develop a model that learns...

Towards Seed-Free Music Playlist Generation: Enhancing collaborative Filtering with Playlist Title Information

In this paper, we propose a hybrid Neural Collaborative Filtering (NCF) model trained with a multi-objective function to achieve a music playlist generation system. The proposed approach focuses particularly on the cold-start problem (playlists with no seed tracks) and uses a text encoder employing a Recurrent Neural Network (RNN) to exploit...

Ranking and Context-awareness in Recommender Systems

In this thesis we report the results of our research on recommender systems, which addresses some of the critical scientific challenges that still remain open in this domain. Collaborative filtering (CF) is the most common technique of predicting the interests of a user by collecting preference information from many users. In order to determine...