In this article, we present an extensive survey of music information retrieval (MIR) research into music playlists. Our survey spans more than 20 years, and includes around 300 papers about playlists, with over 70 supporting sources. It is the first survey that is self-contained in the sense that it combines all the different MIR research into playlists. It embraces topics such as algorithms for automatic generation, for automatic continuation, for assisting with manual generation, for tagging and for captioning. It looks at manually constructed playlists, both those that are constructed for and by individuals and those constructed in collaboration with others. It covers ground-breaking research into enhancing playlists by cross-fading consecutive songs and by interleaving consecutive songs with speech, similar to what happens on a radio show. Most significantly, it is the first survey that can fully incorporate the paradigm shift that has taken place in the way people consume recorded music: the shift from physical media to music streaming. This has wrought profound changes in the size of music collections available to listeners and thus the algorithms that support the construction, curation and presentation of playlists and the methods adopted by users when they also construct, curate and listen to playlists.

Playlists are a major way of interacting with music, as evidenced by the fact that streaming services currently host billions of playlists. In this content overload scenario, it is crucial to automatically characterise playlists, so that music can be effectively organised, accessed and retrieved. One way to characterise playlists is by their listening context. For example, one listening context is “workout”, which characterises playlists suited to be listened to by users while working out. Recent work attempts to predict the listening contexts of playlists, formulating the problem as multi-label classification. However, current classifiers for listening context prediction are limited in the input data modalities that they handle, and on how they leverage the inputs for classification. As a result, they achieve only modest performance. In this work, we propose to use knowledge graphs to handle multi-modal inputs, and to effectively leverage such inputs for classification. We formulate four novel classifiers which yield approximately 10% higher performance than the state-of-the-art. Our work is a step forward in predicting the listening contexts of playlists, which could power important real-world applications, such as context-aware music recommender systems and playlist retrieval systems.

Music streaming services feature billions of playlists created by users, professional editors or algorithms.In this content overload scenario, it is crucial to characterise playlists, so that music can be effectively organised and accessed.Playlist titles and descriptions are proposed in natural language either manually by music editors and users or automatically from pre-defined templates.However, the former is time-consuming while the latter is limited by the vocabulary and covered music themes.In this work, we propose PLAYNTELL, a dataefficient multi-modal encoder-decoder model for automatic playlist captioning.Compared to existing music captioning algorithms, PLAYN-TELL leverages also linguistic and musical knowledge to generate correct and thematic captions.We benchmark PLAYNTELL on a new editorial playlists dataset collected from two major music streaming services.PLAYN-TELL yields 2x-3x higher BLEU@4 and CIDEr than state of the art captioning algorithms.

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, i.e. as a plain list of songs. An alternative are tours, where the songs alternate with segues, which explain the connections between consecutive songs. Tours address the user need of seeking background information about songs, and are found to be superior to playlists, given the right user context. In this work, we provide, for the first time, a user-centered evaluation of two tour-generation algorithms (Greedy and Optimal) using semi-structured interviews. We assess the algorithms, we discuss attributes of the tours that the algorithms produce, we identify which attributes are desirable and which are not, and we enumerate several possible improvements to the algorithms, along with practical suggestions on how to implement the improvements. Our main findings are that Greedy generates more likeable tours than Optimal, and that three important attributes of tours are segue diversity, song arrangement and song familiarity. More generally, we provide insights into how to present music to users, which could inform the design of user-centered recommender systems.

In the music domain, repeated consumption is not uncommon. In this work, we explore how to recommend familiar music in fresh ways. Specifically, we design algorithms that can produce ‘tours’ through a small personal collection of songs. The tours are decorated with segues, which are textual connections between consecutive songs, chosen for their interestingness. We present three such algorithms, and we outline their strengths and weaknesses based on a comparative offline evaluation. This preliminary algorithmic work is a prelude to upcoming user-centric investigations.

We introduce a novel domain-independent algorithm for generating interesting item-to-item textual connections, or segues. Pivotal to our contribution is the introduction of a scoring function for segues, based on their ‘interestingness’. We provide an implementation of our algorithm in the music domain. We refer to our implementation as Dave. Dave is able to generate 1553 different types of segues, that can be broadly categorized as either informative or funny. We evaluate Dave by comparing it against a curated source of song-to-song segues, called The Chain. In the case of informative segues, we find that Dave can produce segues of the same quality, if not better, than those to be found in The Chain. And, we report positive correlation between the values produced by our scoring function and human perceptions of segue quality. The results highlight the validity of our method, and open future directions in the application of segues to recommender systems research.