Generating Interesting Song-to-Song Segues With Dave

Gabbolini, Giovanni; Bridge, Derek

doi:10.1145/3450613.3456819

Cited by 3 publications

(18 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As we have already indicated, our approach to the measurement of similarity of entities in knowledge graphs is path-based. It builds on work described in [4], where the authors introduce interestingness as a way of distinguishing more interesting from less interesting paths in a knowledge graph.…”

Section: Methodsmentioning

confidence: 99%

“…more interesting from less interesting paths in a knowledge graph. In [4], the authors use what they call segues to connect a song to the next song, e.g. in a playlist.…”

Section: 56mentioning

confidence: 99%

“…Segues are translations into natural language of interesting paths in a knowledge graph. We also employ the path-to-text module from [4], useful to translate paths to natural language. In this paper, we are extending [4] by showing that these paths and their interestingness scores can be the basis of an accurate and interpretable similarity measure.…”

Section: 56mentioning

confidence: 99%

“…We also employ the path-to-text module from [4], useful to translate paths to natural language. In this paper, we are extending [4] by showing that these paths and their interestingness scores can be the basis of an accurate and interpretable similarity measure. For example, given the artists Tammy Wynette and George Jones as seed and target, our algorithm finds six paths between the two items, then it assigns an interestingness score to the paths, and it aggregates the scores to determine the similarity.…”

Section: 56mentioning

confidence: 99%

“…We propose to score paths based on interestingness. Interestingness is introduced in [4] as a way of distinguishing Path (in natural language)…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An Interpretable Music Similarity Measure Based on Path Interestingness

Gabbolini,

Bridge

2021

Preprint

Self Cite

View full text Add to dashboard Cite

We introduce a novel and interpretable path-based music similarity measure. Our similarity measure assumes that items, such as songs and artists, and information about those items are represented in a knowledge graph. We find paths in the graph between a seed and a target item; we score those paths based on their interestingness; and we aggregate those scores to determine the similarity between the seed and the target. A distinguishing feature of our similarity measure is its interpretability. In particular, we can translate the most interesting paths into natural language, so that the causes of the similarity judgements can be readily understood by humans. We compare the accuracy of our similarity measure with other competitive path-based similarity baselines in two experimental settings and with four datasets. The results highlight the validity of our approach to music similarity, and demonstrate that path interestingness scores can be the basis of an accurate and interpretable similarity measure.

show abstract

Section: Methodsmentioning

confidence: 99%

“…more interesting from less interesting paths in a knowledge graph. In [4], the authors use what they call segues to connect a song to the next song, e.g. in a playlist.…”

Section: 56mentioning

confidence: 99%

Section: 56mentioning

confidence: 99%

Section: 56mentioning

confidence: 99%

“…We propose to score paths based on interestingness. Interestingness is introduced in [4] as a way of distinguishing Path (in natural language)…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Interpretable Music Similarity Measure Based on Path Interestingness

Gabbolini,

Bridge

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

A User-Centered Investigation of Personal Music Tours

Gabbolini¹,

Bridge²

2022

Proceedings of the 16th ACM Conference on Recommender Systems

Self Cite

View full text Add to dashboard Cite

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 (G and O ) 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 G generates more likeable tours than O , 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.

show abstract

Surveying More Than Two Decades of Music Information Retrieval Research on Playlists

Gabbolini,

Bridge

2024

ACM Trans. Intell. Syst. Technol.

View full text Add to dashboard Cite

In this paper, 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.

show abstract

Generating Interesting Song-to-Song Segues With Dave

Cited by 3 publications

References 22 publications

An Interpretable Music Similarity Measure Based on Path Interestingness

An Interpretable Music Similarity Measure Based on Path Interestingness

A User-Centered Investigation of Personal Music Tours

Surveying More Than Two Decades of Music Information Retrieval Research on Playlists

Contact Info

Product

Resources

About