Table 1
Summary of terms and definitions presented in the overview.
| Terms & Definitions | Reference(s) |
|---|---|
| Cultural diversity: the uniqueness and plurality of the identities of the groups and societies making up humankind. | UNESCO (2001) Huron (2004) |
| Dual-concept diversity: measurement of diversity based on the variety and balance of the elements of a population divided into categories. Variety: number of categories in a population. Balance: distribution of elements over the categories of a population. Disparity: differences between categories of a population. | McDonald and Dimmick (2003) Stirling (2007) |
| Diversity by design: the creation of an architecture or service that helps people to make diverse choices. Source diversity: the range of information providers. Content diversity: the range of information provided. Exposure diversity: the range of information accessed by people. Individual autonomy perspective: provide people with a tool for exploiting their different interests. Deliberative perspective: promote public awareness by showing divergent opinions. Adversarial perspective: enhance the visibility of underrepresented opinions. | Napoli (1999) Helberger (2011) Helberger el al. (2018) Loecherbach et al. (2020) |
| Diversity-aware RS: recommender systems designed to diversify the users’ experience. Item diversity: the range of items recommended by a RS. User diversity: the range of users interacting with a RS. (User) behavioural diversity: the range of items accessed by the users. (User) perceived diversity: the item diversity as perceived by the users. | Castells et al. (2015) Kaminskas and Bridge (2016) Kunaver and Požrl (2017) |
Figure 1
Mind map of elements constituting Music RS diversity. Behavioural diversity, for instance represented by listening events, is measured when users access the information provided by the items (exposure, Section 2.1). These connection points rely on one side on the item diversity (Section 3.1), built on content and source item features, and on the other side on user diversity (Section 3.2), with regards to their characteristics. Additionally, perceived diversity (Section 3.2.1) creates a bridge between the Esthetic and Poietic domains.
Table 2
List of works analyzing users’ behavioural diversity in the music domain, presented in chronological order.
| Reference | Diversity metric definition(s) Dataset(s) |
|---|---|
| Farrahi et al. (2014) | • Number of unique genres associated with the artists listened to by a user. MMTD (Hauger et al., 2013). |
| Schedl and Hauger (2015) | • Users’ average track listening frequency; number of distinct track genres. Last.fm LEs. |
| Ferwerda et al. (2016b) | • Aggregation of each user’s listening history by artist and genre. LFM-1b (Schedl, 2016). |
| Ferwerda and Schedl (2016) | • Overall volume of genre occurrences; relative listening volume exceeding one per mille; Shannon index computed over artist genre. LFM-1b (Schedl, 2016). |
| Park et al. (2015) | • Rao-Stirling index computed over artist genre. Last.fm users’ top artists. |
| Datta et al. (2018) | • Log number of unique artists, songs, and genres listened to; number of unique top artists in a user’s geographic region divided by the number of unique artists listened to over the same time period; Herfindahl index computed over a user’s weekly plays. Spotify LEs. |
| Wang et al. (2018) | • Ratio of unique artists in a user’s playlists over all the artists listened to by the user; same ratio computed over artist genre. Last.fm 1K (Celma, 2010). |
| Li et al. (2018) | • Hill-type true diversity (Rao-Stirling index) computed over album genre. Xiami LEs. |
| Way et al. (2019) | • Rao-Stirling index computed over artist genre. Spotify LEs. |
| Poulain and Tarissan (2020) | • Herfindahl-Hirschman index computed over tripartite graphs (users, tracks, tags). MSD (Bertin-Mahieux et al., 2011); Amazon Dataset (McAuley et al., 2015). |
| Anderson et al. (2020) | • Average cosine similarity between a track embedding and the average of the user’s track embeddings. Spotify LEs. |
| Kowald et al. (2021) | • Cosine similarity computed over the users’ track genre distributions. LFM-BeyMS (Kowald et al., 2021), subset of LFM-1b (Schedl, 2016). |
Table 3
List of works analysing item diversity in the music domain, presented in chronological order. We refer to Ziegler et al. (2005) for the formula of the Intra-List Diversity (ILD).
| Reference | Diversity metric definition(s) Dataset(s) |
|---|---|
| Slaney and White (2006) | • Distribution of points in an 11-dimensional genre space computed over tracks’ acoustic features. WebJay playlists. |
| Ferwerda et al. (2017a) | • ILD using Euclidean distance computed over the latent factor of item-user matrix factorisation. Last.fm LEs; LFM-1b (Schedl, 2016). |
| Lu and Tintarev (2018) | • ILD computed over weighted combinations of several diversity degrees for different attributes (release time, artist, genre, tempo, key). Spotify users’ preferred songs; Echo Nest Taste Profile Subset (Bertin-Mahieux et al., 2011). |
| Porcaro and Gómez (2019) | • ILD using cosine distance computed over track tag embeddings. Art of the Mix playlists (Berenzweig et al., 2004); Yes.com radio playlists (Chen et al., 2012); MMTD (Hauger et al., 2013); Deezer users’ playlists. |
| Knees and Hübler (2019) | • Simpson index computed over tracks’ record labels. MPD (Chen et al., 2018). |
| Robinson et al. (2020) | • ILD using Euclidean distance computed over the latent factor of item-user matrix factorisation. Last.fm LEs. |
| Jin et al. (2020) | • ILD using Jaccard Index computed over track genre. Spotify users’ recommendations. |