DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation

In recent years, there has been growing focus on the study of automated recommender systems. Music recommendation systems serve as a prominent domain for such works, both from an academic and a commercial perspective. A fundamental aspect of music perception is that music is experienced in temporal context and in sequence. In this work we present DJ-MC, a novel reinforcement-learning framework for music recommendation that does not recommend songs individually but rather song sequences, or playlists, based on a model of preferences for both songs and song transitions. The model is learned online and is uniquely adapted for each listener. To reduce exploration time, DJ-MC exploits user feedback to initialize a model, which it subsequently updates by reinforcement. We evaluate our framework with human participants using both real song and playlist data. Our results indicate that DJ-MC's ability to recommend sequences of songs provides a significant improvement over more straightforward approaches, which do not take transitions into account.Comment: -Updated to the most recent and completed version (to be presented at AAMAS 2015) -Updated author list. in Autonomous Agents and Multiagent Systems (AAMAS) 2015, Istanbul, Turkey, May 201

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

A Hybrid Approach to Music Playlist Continuation Based on Playlist-Song Membership

Automated music playlist continuation is a common task of music recommender systems, that generally consists in providing a fitting extension to a given playlist. Collaborative filtering models, that extract abstract patterns from curated music playlists, tend to provide better playlist continuations than content-based approaches. However, pure collaborative filtering models have at least one of the following limitations: (1) they can only extend playlists profiled at training time; (2) they misrepresent songs that occur in very few playlists. We introduce a novel hybrid playlist continuation model based on what we name "playlist-song membership", that is, whether a given playlist and a given song fit together. The proposed model regards any playlist-song pair exclusively in terms of feature vectors. In light of this information, and after having been trained on a collection of labeled playlist-song pairs, the proposed model decides whether a playlist-song pair fits together or not. Experimental results on two datasets of curated music playlists show that the proposed playlist continuation model compares to a state-of-the-art collaborative filtering model in the ideal situation of extending playlists profiled at training time and where songs occurred frequently in training playlists. In contrast to the collaborative filtering model, and as a result of its general understanding of the playlist-song pairs in terms of feature vectors, the proposed model is additionally able to (1) extend non-profiled playlists and (2) recommend songs that occurred seldom or never in training~playlists

Music feature extraction and analysis through Python

En l'era digital, plataformes com Spotify s'han convertit en els principals canals de consum de música, ampliant les possibilitats per analitzar i entendre la música a través de les dades. Aquest projecte es centra en un examen exhaustiu d'un conjunt de dades obtingut de Spotify, utilitzant Python com a eina per a l'extracció i anàlisi de dades. L'objectiu principal es centra en la creació d'aquest conjunt de dades, emfatitzant una àmplia varietat de cançons de diversos subgèneres. La intenció és representar tant el panorama musical més tendenciós i popular com els nínxols, alineant-se amb el concepte de distribució de Cua Llarga, terme popularitzat com a "Long Tail" en anglès, que destaca el potencial de mercat de productes de nínxols amb menor popularitat. A través de l'anàlisi, es posen de manifest patrons en l'evolució de les característiques musicals al llarg de les dècades passades. Canvis en característiques com l'energia, el volum, la capacitat de ball, el positivisme que desprèn una cançó i la seva correlació amb la popularitat sorgeixen del conjunt de dades. Paral·lelament a aquesta anàlisi, es concep un sistema de recomanació musical basat en el contingut del conjunt de dades creat. L'objectiu és connectar cançons, especialment les menys conegudes, amb possibles oients. Aquest projecte ofereix perspectives beneficioses per a entusiastes de la música, científics de dades i professionals de la indústria. Les metodologies implementades i l'anàlisi realitzat presenten un punt de convergència de la ciència de dades i la indústria de la música en el context digital actualEn la era digital, plataformas como Spotify se han convertido en los principales canales de consumo de música, ampliando las posibilidades para analizar y entender la música a través de los datos. Este proyecto se centra en un examen exhaustivo de un conjunto de datos obtenido de Spotify, utilizando Python como herramienta para la extracción y análisis de datos. El objetivo principal se centra en la creación de este conjunto de datos, enfatizando una amplia variedad de canciones de diversos subgéneros. La intención es representar tanto el panorama musical más tendencioso y popular como los nichos, alineándose con el concepto de distribución de Cola Larga, término popularizado como Long Tail en inglés, que destaca el potencial de mercado de productos de nichos con menor popularidad. A través del análisis, se evidencian patrones en la evolución de las características musicales a lo largo de las décadas pasadas. Cambios en características como la energía, el volumen, la capacidad de baile, el positivismo que desprende una canción y su correlación con la popularidad surgen del conjunto de datos. Paralelamente a este análisis, se concibe un sistema de recomendación musical basado en el contenido del conjunto de datos creado. El objetivo es conectar canciones, especialmente las menos conocidas, con posibles oyentes. Este proyecto ofrece perspectivas beneficiosas para entusiastas de la música, científicos de datos y profesionales de la industria. Las metodologías implementadas y el análisis realizado presentan un punto de convergencia de la ciencia de datos y la industria de la música en el contexto digital actualIn the digital era, platforms like Spotify have become the primary channels of music consumption, broadening the possibilities for analyzing and understanding music through data. This project focuses on a comprehensive examination of a dataset sourced from Spotify, with Python as the tool for data extraction and analysis. The primary objective centers around the creation of this dataset, emphasizing a diverse range of songs from various subgenres. The intention is to represent both mainstream and niche musical landscapes, aligning with the Long Tail distribution concept, which highlights the market potential of less popular niche products. Through analysis, patterns in the evolution of musical features over past decades become evident. Shifts in features such as energy, loudness, danceability, and valence and their correlation with popularity emerge from the dataset. Parallel to this analysis is the conceptualization of a music recommendation system based on the content of the data set. The aim is to connect tracks, especially lesser-known ones, with potential listeners. This project provides insights beneficial for music enthusiasts, data scientists, and industry professionals. The methodologies and analyses present a convergence of data science and the music industry in today's digital contex

Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design

It is well known that collaborative filtering (CF) based recommender systems provide better modeling of users and items associated with considerable rating history. The lack of historical ratings results in the user and the item cold-start problems. The latter is the main focus of this work. Most of the current literature addresses this problem by integrating content-based recommendation techniques to model the new item. However, in many cases such content is not available, and the question arises is whether this problem can be mitigated using CF techniques only. We formalize this problem as an optimization problem: given a new item, a pool of available users, and a budget constraint, select which users to assign with the task of rating the new item in order to minimize the prediction error of our model. We show that the objective function is monotone-supermodular, and propose efficient optimal design based algorithms that attain an approximation to its optimum. Our findings are verified by an empirical study using the Netflix dataset, where the proposed algorithms outperform several baselines for the problem at hand.Comment: 11 pages, 2 figure