SeER: An Explainable Deep Learning MIDI-based Hybrid Song Recommender System

State of the art music recommender systems mainly rely on either matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based filtering or predict the next user interaction by learning from temporal sequences of user actions. Despite advances in deep learning for song recommendation, none has taken advantage of the sequential nature of songs by learning sequence models that are based on content. Aside from the importance of prediction accuracy, other significant aspects are important, such as explainability and solving the cold start problem. In this work, we propose a hybrid deep learning model, called “SeER , that uses collaborative filtering (CF) and deep learning sequence models on the MIDI content of songs for recommendation in order to provide more accurate personalized recommendations; solve the item cold start problem; and generate a relevant explanation for a song recommendation. Our evaluation experiments show promising results compared to state of the art baseline and hybrid song recommender systems in terms of ranking evaluation. Moreover, based on proposed tests for offline validation, we show that our personalized explanations capture properties that are in accordance with the user’s preferences

An explainable sequence-based deep learning predictor with applications to song recommendation and text classification.

Streaming applications are now the predominant tools for listening to music. What makes the success of such software is the availability of songs and especially their ability to provide users with relevant personalized recommendations. State of the art music recommender systems mainly rely on either Matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based filtering or predict the next user interaction (listening to a song) using a memory-based deep learning structure that learns from temporal sequences of user actions. Despite advances in deep learning models for song recommendation systems, none has taken advantage of the sequential nature of songs by learning sequence models that are based on content. Aside from the importance of prediction accuracy in recommendation systems, recent research has unveiled the importance of other significant aspects such as explainability and solving the cold start problem where a new user or item with no prior history of interactions joins an online platform. In this work, we propose a hybrid deep learning structure, called “SeER”, that uses collaborative filtering and deep sequence models on the MIDI content of songs for recommendation. Our approach aims to take advantage of the superior capabilities of re-current neural networks, the multidimensional time series aspect of songs, and the power of matrix factorization to: •provide more accurate personalized recommendations, •solve the item cold start problem which is in the case of where a new unrated song is added to the set of choices to recommend; and •generate a relevant explanation for a song recommendation using a novel explainability process we named “Segment Forward Propagation Explainability”. Our evaluation experiments show promising results compared to state of the art baseline and hybrid song recommender systems in terms of ranking evaluation. In addition, we demonstrate how our explanation mechanism can be used with generic sequential data beyond music, namely unstructured free text in two application domains: sentiment classification of online user reviews and delineating potential child abuse instances from medical examination reports

Feature-combination hybrid recommender systems for automated music playlist continuation

Music recommender systems have become a key technology to support the interaction of users with the increasingly larger music catalogs of on-line music streaming services, on-line music shops, and personal devices. An important task in music recommender systems is the automated continuation of music playlists, that enables the recommendation of music streams adapting to given (possibly short) listening sessions. Previous works have shown that applying collaborative filtering to collections of curated music playlists reveals underlying playlist-song co-occurrence patterns that are useful to predict playlist continuations. However, most music collections exhibit a pronounced long-tailed distribution. The majority of songs occur only in few playlists and, as a consequence, they are poorly represented by collaborative filtering. We introduce two feature-combination hybrid recommender systems that extend collaborative filtering by integrating the collaborative information encoded in curated music playlists with any type of song feature vector representation. We conduct off-line experiments to assess the performance of the proposed systems to recover withheld playlist continuations, and we compare them to competitive pure and hybrid collaborative filtering baselines. The results of the experiments indicate that the introduced feature-combination hybrid recommender systems can more accurately predict fitting playlist continuations as a result of their improved representation of songs occurring in few playlists(VLID)328909

Machine learning approaches to hybrid music recommender systems

Music catalogs in music streaming services, on-line music shops and private collections become increasingly larger and consequently difficult to navigate. Music recommender systems are technologies devised to support users accessing such large catalogs by automatically identifying and suggesting music that may interest them. This thesis focuses on the machine learning aspects of music recommendation with contributions at the intersection of recommender systems and music information retrieval: I investigate and propose recommender systems that observe and exploit the particularities of the music domain. The thesis specializes in "hybrid" music recommender systems, so called because they combine two fundamentally different types of data: (1) user-music interaction histories (e.g., the music that users recently listened to, or "liked"), with (2) descriptions of the musical content (e.g., the genre, or acoustical properties of a song). The proposed hybrid music recommender systems integrate the strengths of these two types of data into enhanced standalone systems. This is in contrast to most previous approaches in the literature, where hybridization was achieved through the heuristic combination of music recommendations issued by independent systems. The proposed hybrid music recommender systems are thoroughly evaluated against competitive recommender system baselines, for different music recommendation tasks, and on different datasets. According to the conducted experiments, the proposed systems predict music recommendations comparably or more accurately than the considered baselines, with the improvements being largely explained by their superior ability to handle infrequent music items. In this way, the proposed hybrid music recommender systems provide means to alleviate the so-called "cold-start" problem for new releases and infrequent music and enable the discovery of music beyond the charts of popular music. Special attention is paid to the particularities of the music domain. I focus on two important music recommendation tasks: music artist recommendation, focusing on general, stable user music preferences, and music playlist continuation, focusing on local relationships in short listening sessions. I exploit data sources abundant in the context of on-line music consumption: user listening histories, hand-curated music playlists, music audio signal, and social tags. I investigate challenges specific to modeling music playlists: the choice and the arrangement of songs within playlists, and the effectiveness of different types of music descriptions to identify songs that fit well together.submitted by Andreu Vall PortabellaUniversität Linz, Dissertation, 2018(VLID)336676