Naturally, the listening histories produced by these real-world services are not nearly as complex as the ideal version discussed above. However, they already contain enough information to provide useful applications. In the following, I will take the example of last.fm which represents the minimal version of a listening history containing for each listening instance only asong identifierand atime stamp.
Thesong identifierprovides a unique description of the song that has been listened to. In last.fm’s case this contains the artist’s name and the name of the track. They acquire this information either through the provided ID3-metadata of a song or audio fingerprinting, where a short acoustic sample of the song is sent to last.fm to be compared to a database of existing songs 26. Last.fm also tries to correct typos in a track or artist name and
attaches terms such as ’(live)’ for live-versions of songs.
Thetime stampof a listening instance contains the time and date a song has been listened to. Providing the correct time zone for the listening instance is not possible without locating the listeners, so this information is kept internally in the UTS (Unix Time Stamp) format, which provides the seconds since midnight January 1st, 1970 in the UTC±0 time zone. Each time stamp has thus an accuracy of one second and contains the starting point of the song instance.
One peculiarity of last.fm’s listening histories is also that they do not contain the dura- tion of the listening instance. This stems from the original intention of capturing listen- ing histories as input for a recommender system that only requires positive or negative votes for each song. This intention also led to the Audioscrobbler’s default behavior of only logging a listening instance if a song has been heard for at least 30 seconds (i.e., no skipping has occurred until then). This setting is however adjustable.
Having a uniquely identifiable song means that various other data sources can be ac- cessed to recreate some of the contextual information that is necessary for benefiting from listening histories (for the following cf. [13]). One common scheme for this meta- data is the hierarchy of songs, albums, artists, and genres (see figure 3.1).
Organizing something as diverse is music can be difficult but the sheer number of songs requires some form of classification. "A common (but vague) abstraction that com- bines content- and contextual information is the musical genre. Genres such as ’Rock’, ’Pop’ and ’R’n’B’ describe not only a defining sound and style but also a certain con- text of the music (e.g., ’Brit-Pop’). Genres are hierarchical in nature (’Alternative Rock’ is a sub-genre of the more general ’Rock’) and this hierarchy is commonly extended to artists and their albums"[13]. Genres are, however, not without their problems [7]. They are not necessarily accurate or clearly defined (a problem for studies based on self-documentation by the participants, cf. [135]). Applying a strict hierarchy of genres, artists, and albums also means that one artist necessarily belongs to one genre which restricts such a classification to a musically-stable set of artists. Yet, implementing a
26R. Jones. Last.fm - The Blog: Audio Fingerprinting for clean Metadata, http://blog.last.fm/2007/08/29/audio- fingerprinting-for-clean-metadata
Listening Session Listening Session
Time
Hierarchy
Genre Sub-Genre Artists Albums Songs User-generated Keywords ……Figure 3.1The space of (contemporary) music can be divided along the lines of single songs, albums, artists, and (sub-)genres. User-generated keywords or tags can provide additional information to the strict hierarchy (Source: [13]).
directed graph instead of such a tree would be problematic in the commonly used table- based media players (e.g., iTunes).
One way to overcome the restrictions of a hierarchy is to add user-generated keywords or tags. Last.fm lets listeners provide their own keywords for songs and also makes these tags available to the community. Using this wisdom of crowds [158], songs can be classified simply by taking the statistically most common keywords from the com- munity. Regarding the contents of tags, Firan et al. identified 21,177 different keywords and found that: "... the top 100 most used tags showed that approximately 60% of the tags represent genre descriptions, while the rest of 40% is shared among tags describing personal impressions (e.g., ’seen live’), artists (e.g., ’female vocalists’), time period (e.g., ’80s’), country of provenience, soundtrack, tempo, or instruments" [49]. The second cat- egory of personal impressions can also give insight into some of the lacking information from local and personal context. In a follow-up analysis, Bischoff et al. showed [18] that personal last.fm tags obeyed to the classification for user-generated keywords provided by Golder & Huberman [55]. This classification also contains categories forself reference
("seen live") andusage context("workout", "study")[18] and can thus amend the available listening information.
contextual information: People might not be motivated to manually annotate all of their songs, leading to a semi-tagged collection that is too inaccurate to be useful. User- generated tags also have the usual problems of self-documentation and biases. Finally, due to their being costly to produce, tags are static and cannot reflect the ever-changing local context.
Last.fm not only provides tracks listening histories but also works as a social network. Profile owners can friend others and also style their profiles to reflect their personalities. This also means that last.fm collects and presents demographic information (gender, age, country of residence) for each of their registered listeners. Non-structured informa- tion can also be added in the ’About Me’ textbox. All of this, however, is optional and prone to erroneous or fake entries.