=Paper=
{{Paper
|id=None
|storemode=property
|title=Using Song Social Tags and Topic Models to Describe and Compare Playlists
|pdfUrl=https://ceur-ws.org/Vol-633/wom2010_paper2.pdf
|volume=Vol-633
}}
==Using Song Social Tags and Topic Models to Describe and Compare Playlists==
https://ceur-ws.org/Vol-633/wom2010_paper2.pdf
Using Song Social Tags and Topic Models to Describe and
Compare Playlists
Ben Fields, Christophe Rhodes and Mark d’Inverno
Department of Computing
Goldsmiths University of London
New Cross
London, SE14 6NW
United Kingdom
[b.fields | c.rhodes | dinverno]@gold.ac.uk
ABSTRACT or will be played after the current recommended song. Yet
Playlists are a natural delivery method for music recom- little is understood about how playback order affects the
mendation and discovery systems. Recommender systems success or failure of a recommendation of a piece of music.
offering playlists must strive to make them relevant and en- Whether a system makes user-based, object-based or hybrid
joyable. In this paper we survey many current means of gen- recommendations, a better awareness and use of playback
erating and evaluating playlists. We present a means of com- order will yield an improved music recommender system.
paring playlists in a reduced dimensional space through the In order to take advantage of the effect of playback order,
use of aggregated tag clouds and topic models. To evaluate it is necessary to have some means of comparing playlists
the fitness of this measure, we perform prototypical retrieval with one another. While ratings-based generic recommender
tasks on playlists taken from radio station logs gathered from strategies could be employed, such techniques could only
Radio Paradise and Yes.com, using tags from Last.fm with be used in systems which allow for the rating of playlists
the result showing better than random performance when directly (as opposed to the much more common rating of
using the query playlist’s station as ground truth, while fail- member songs).Alternatively, n distance measure between
ing to do so when using time of day as ground truth. We then playlists can be used to facilitate the prediction and gen-
discuss possible applications for this measurement technique eration of well-ordered lists of song sequences for recom-
as well as ways it might be improved. mendation. This has the advantage being applicable to the
vast majority of existing playlist generation systems, many
of which do not to collect playlist level ratings from their
Categories and Subject Descriptors users. Further, a measure of playlist distance has a number
H.5.5 [Sound and Music Computing]: Signal analysis, of other applications in music recommender and discovery
synthesis, and processing; H.5.1 [Multimedia Informa- systems including label propagation, predictive personaliza-
tion Systems]: Evaluation/methodology tion and context tuning to name a few.
In this paper we propose an objective distance measure be-
Keywords tween playlists. To better understand why such a measure
is needed, Section 2 provides background information in ex-
LDA, Topic Models, playlists, music, similarity, information isting playlist generation and evaluation techniques. While
retrieval, metric space, social tags any sufficiently expressive and low-dimensional feature is
compatible with our playlist measure, we use a novel so-
1. INTRODUCTION cial tag-based feature in this paper. This song-level feature
Inherent to the design of any recommender or retrieval is detailed in Section 3. This is followed by an explanation
system is a means of display or delivery of selected content. of our distance measurement itself in Section 4. Putting
For a system that recommends music this means playback this into practice, we detail some proof of concept evalua-
of an audio file. Listening to or playing a piece of music tion in Section 5. We discuss the results of this evaluation
take the length time of that piece of music. Given this link and possible extensions in Section 6.
between music and time, when considering what information
is relevant for a recommendation it is vital to consider the 2. PLAYLIST AS DELIVERY MECHANISM
context of time; that is, what music has been played before
In this section we survey the use of playlists in the de-
livery of content in existing recommendation and retrieval
systems. This is followed by a review of current evaluation
methods for generated playlists. These two survey points
will show both the widespread use of playlist generation in
WOMRAD 2010 Workshop on Music Recommendation and Discovery, music recommendation and discovery systems and the need
colocated with ACM RecSys 2010 (Barcelona, SPAIN) for more quality evaluation of these systems.
Copyright c . This is an open-access article distributed under the terms While this brief survey is focused on automatic playlist
of the Creative Commons Attribution License 3.0 Unported, which permits
unrestricted use, distribution, and reproduction in any medium, provided generation, there is a wealth of both academic and lay work
the original author and source are credited. discussing various aspects manual human-driven playlist con-
struction that may be of interest to the reader. Work in this A recent approach uses co-occurrence in n-grams extracted
area tends to deal with radio (e.g. [1]) or club and dance disc from the internet radio station Radio Paradise1 to deform
jockeys (e.g. [13]), being the two principal areas where the a content-based similarity space [26]. This deformed space
explicit construction of ordered lists of songs are tied to the is then used in a manner that is similar to [18] to generate
field. It is with these areas of manual playlist construction paths from one song to another, minimizing step distance
in mind that we will examine past efforts in both automatic throughout the path.
playlist construction and evaluation techniques. Also of note is [31], which in contrast to most of the pre-
vious systems, uses nearest neighbor co-occurrence in radio
2.1 Usage in the Wild playlist logs to determine song similarity. While the evalua-
tion was preliminary this method shows promise.
There have been many music recommendation and re-
trieval systems that employ some kind of automatic playlist 2.2 Evaluation Methods
construction within their system. Frequently this is done as
a means of content delivery or, less often, as a way of facil- The most prevalent method of evaluation used in playlist
itating human evaluation of an underlying process such as generation systems is direct human evaluation by listening.
content-based music similarity or recommendation. What The system detailed in [29], a rule-based automatic playlist
follows is a brief survey of existing methods of playlist gen- generator that uses features derived from metadata, is simi-
eration both with and without human intervention. lar to [2, 30]. Of note in [29] is the thorough human listener
A web based system for personalized radio is detailed testing which shows the automatic playlist generator per-
in [20]. In this early system users create and publish playlists forming considerably better than songs ordered randomly.
facilitated through a process analogous to collaborative fil- This evaluation, though better than most, still fails to com-
tering. This results in quasi-automatic playlist creation, pare the automatic playlists against human expert playlists.
with any sequence ordering depending entirely on the user. Additionally, to reduce test time, the evaluation uses arbi-
Another variation of the social interaction intermediary is trary one minute clips from the songs rather than the en-
shown in [27], which presents the Jukola system. This sys- tirety of the song or an intentionally chosen segment. A
tem creates playlists via democratic vote on every song us- content-based similarity playlist generator with a novel eval-
ing mobile devices of listeners in the same physical space. uation is seen in [28]. Here the authors track the number
Furthering the ideas of collaborative human generation, [25] times the user presses the skip button to move on from the
shows a system called Social Playlist. This system is based currently playing song. All songs that are skipped are con-
on the idea of social interaction through playlist sharing, sidered false positives and those that are completely played
integrating mobile devices and communal playback. are treated as true positives. From this many standard in-
A fully automatic rule-based system is described in [2]. formation retrieval techniques can be used in the evaluation,
This system uses existing metadata such as artist name, resulting in a rich understanding of the results. Ultimately,
song title, duration and beats per minute. The system is it is still human user listening evaluation though and its
designed from the ground up to be scalable and is shown to biggest drawback is playback time. Assuming an average
work given a database of 200000 tracks. An approach that song length of five minutes it would take an an hour and 40
is derived from recommender systems is seen in [4]. Here the minutes (per listener) to listen to 20 songs with no time for
authors use the ratings and personalization information to the skipped songs. This skip-based evaluation framework is
derive radio for a group. An attempt to optimize a playlist further used in [12] where existing last.fm user logs (which
based on known user preference as encoded in song selection include skip behavior) are analyzed using fuzzy set theory to
patterns is shown in [30]. This effort uses Gaussian process determine playlist generation heuristics in the system. Ad-
regression on user preference to infer playlists. The system ditionally, many systems of playlist generation lack formal
uses existing a priori metadata as the features for selection. evaluation all together.
A means of using webmining derived artist similarity with
content-based song similarity is used to automatically gener-
2.3 Summary
ate playlists in [22]. This system combined these two spaces While a number of techniques have been employed to cre-
in such a way as to minimize the use of signal analysis. A ate playlists for a variety of functions, there exist limited
byproduct of this optimization is improved playlist genera- techniques in the evaluation of generated playlists. These
tion as is shown in a small evaluation with human listeners. evaluation techniques rely heavily on time consuming hu-
The Poolcasting system is detailed in [5, 6]. Poolcasting man evaluation. Beyond that, there is no studied means to
uses dynamic weighting of user preferences within a group of objectively compare one playlist with another. In Section 4
users who are all listening to a common stream with the goal we will propose just such a means. First we will describe a
of minimizing displeasure across the entire group. This re- novel song level feature based on tags. A tag-based feature
sults in a system that is very similar to popular commercial will encode socio-cultural data that is missing from analo-
radio in terms of its output. A method for created playlists gous content-based features, though social tags bring about
using an artist social graph, weighted with acoustic similar- some other problems.
ity is shown in [17]. This method takes a start and end song
and constructs a playlist using maximum flow analysis on the 3. TOPIC-MODELED TAG-CLOUDS
weighted graph. Another technique for playlist construction In order to encode playlists in a low dimensional repre-
based on the selection of paths between the start and end sentation we must first represent their member songs in as a
songs is shown in [18]. In this system content-based similar- low dimensional vector. Here we use a Topic-Modeled Tag
ity is used to project a set of songs onto a 2-D map, then a Cloud (TMTC) as a pseudo-content-based feature, in a way
path is found from the start song to the end song with the
1
goal of minimizing the step size between each member song. http://radioparadise.com
Figure 1: The tag cloud for Bohemian Crapsody by Sickboy, from Last.fm.
β
that is functionally analogous to various pure content-based
methods. Using tags and topic models in this way is novel
and what follows is an explanation of the process of building
this feature.
3.1 Tags as Representation
A tag is a word or phrase used to describe a document
of some kind, typically on the Web. Various kinds of doc-
α θ z w
uments are described using tags on the Web including pho- N
tos2 , videos3 and music4 . An aggregated collection of tags, M
weighted by the number of users who ascribe it to a given
object, is commonly referred to as a tag cloud. Figure 2: The graphic model of LDA [11]. The repli-
Tag clouds get their name from the most common visual- cates are represented as the two boxes. The outer
ization method used with them, where each tag is displayed box M represents the corpus of documents, while
with the font size in proportion to the weight, arranged in the inner box N represents the repeating choice of
a way that resembles a cloud. An example of a tag cloud5 topics and words which make up each document.
can be seen in Figure 1 As can be seen in this example, tag
clouds provide a rich description of the music it describes.
Tags and collections of tags in various forms provide the ba- we require. Topic models are described in [10] as “probabilis-
sis for many techniques within music informatics including tic models for uncovering the underlying semantic structure
recommendation, retrieval and discovery applications [3,23]. of [a] document collection based on a hierarchical Bayesian
In addition to human generated tags being used, there is analysis of the original text.” In topic modeling, a document
some research directed toward the automatic application of is transformed into a bag of words, in which all of the words
tags and inference of associated weights on unlabeled pieces of a document are collected and the frequency of the occur-
of music [7, 9, 16, 21]. rence in recorded. We can use the weighted collection of
tags in a tag cloud as this bag of words, with tags serving
3.2 Reducing the Dimensionality as tokenized words.
There exist some techniques (such as [8]) to determine There are a few different ways of generating topic models;
semantic clustering within a tag cloud; however, these sys- for our feature generation we will be using latent Dirichlet
tems are built to facilitate browsing and do not create a allocation [11], treating each tag cloud as a bag-of-words.
sufficiently reduced dimensional representation. The pre- In LDA, documents (in our case tags clouds of songs) are
vious work of [24] comes the closest to the needed dimen- represented as a mixture of implied (or latent) topics, where
sional reduction, also dealing with social tags for music. This each topic can be described as a distribution of words (or
work, through the use of aspect models and latent seman- here, tags).More formally give the hyper-parameter α, and
tic analysis, brings the dimensionality down into the hun- the conditional multinomial parameter β, Equation 3.2 gives
dreds, while preserving meaning. This order of dimensions the joint topic distribution θ, a set of N topics z and a set
is still too high to compute meaningful distance across multi- of N tags w.
song playlists. A feature with dimensionally of the order 102
N
would suffer from the curse of dimensionality [33]: because Y
of its high dimensionality, any attempt to measure distance p(θ, z, w|α, β) = p(θ|α) p(zn |θ)p(wn |zn , β) (1)
n=1
becomes dominated by noise. However, a technique devel-
oped for improved modelling in text information retrieval, In Figure 2 LDA is shown as a probabilistic graphical model.
topic models provide the reduced dimensional representation In order to create topic models using LDA, we need to spec-
2 ify p(θ|α) and p(zn |θ). We estimate our parameters empir-
e.g. http://flickr.com
3 ically from a given corpus of tag clouds. This estimation
e.g. http://youtube.com
4 is done using variational EM as described in [11].This al-
e.g. http://last.fm or http://musicbrainz.org
5
This tag cloud is for the track Bohemian Crapsody by the lows topic distributions to be generated in an unsupervised
artist Sickboy. The tags and the rendering both come from fashion, though the number of topics in a corpus must be
last.fm, available at http://www.last.fm/music/Sickboy/ specified a priori.
_/Bohemian+Crapsody/+tags Once the LDA model is generated, it is used to infer the
complete playlist in a dataset. The distance between two
playlists is then the minimum distance between any two
gather tags for all songs length i sub-vectors drawn from each playlist. One effect of
this technique is easy handling of playlists of unequal length.
This type of distance measurement has been used with
success on sequences of audio frames [14, 15]. The distance
measure in use between vectors can also be changed. In par-
ticular there has been work showing that statistical features
(such as topic models) may benefit from the use of Manhat-
create LDA model describing tan distance [19], however for our prototypical evaluation we
topic distributions have used simple Euclidean distance as seen in equation ??
above.
5. EVALUATION
The goal of our evaluation is to show the fitness of our
distance measurement through preliminary retrieval tests:
infer topic mixtures for all searching for playlists that start at the same time of day as
songs our query playlist and searching for the playlists from the
same station from a database of stations of the same genre.
We examine the logs of a large collection of radio stations,
exhaustively searching example sets. Through precision and
recall we see that our measure organizes playlists in a pre-
dictable and expected way.
create vector database 5.1 Dataset
of playlists In order to test these proposed techniques a collection
of radio station logs were gathered. These logs come from
a collection of broadcast and online stations gathered via
Figure 3: The complete process for construction of Yes.com7 . The logs cover the songs played by all indexed
a TCTM feature set. stations between 19-26 March 2010. For our evaluation task
using this data source we looked at subsets of this com-
plete capture, based on genre labels applied to these sta-
mixture of topics present in the tag cloud for a given song. tions. Specifically we examine stations of the genres rock
This is done via variational inference which is shown in [11] and jazz. The complete Yes.com dataset also includes sta-
to estimate the topic mixture of a document by iteratively tions in the following genre categories: Christian, Country,
minimizing the KL divergence from variational distribution Electronica, Hip-Hop, Latin, Metal, Pop, Punk, R&B/Soul,
of the latent variables and the true posterior p(θ, z|w, α, β). Smooth Jazz and World. These labels are applied by the sta-
This process in it’s entirety is shown as a block diagram tions themselves and the categories are curated by Yes.com.
in Figure 3. Once this process is completed for every song Additionally, the play logs from Radio Paradise8 from 1 Jan-
in our dataset, we will have a single vector with a dimen- uary 2007 to 28 August 2008 form a second set. We then
sionality equal to the number of topics in our LDA whose attempted to retrieve tag clouds from Last.fm9 for all songs
entries indicate topic occupancy for that song. in these logs. When tags were not found the song and its
associated playlist were removed from our dataset
These logs are then parsed into playlists. For the radio
4. PLAYLISTS AS A SEQUENCE OF TOPIC logs retrieved via the Yes api, the top of every hour was used
WEIGHTS as a segmentation point as a facsimile for the boundary be-
Given the single vector per song reduction, we represent tween distinct programs. This is done under the assumption
the playlists these song are in as ordered sequences of these that program are more likely than not to start and finish
vectors. Thus each playlist is represented as a l×d-dimensional on the hour in US commercial broadcast. Note that this
vector, where l is the number of songs in a given playlist and method of boundary placement will almost certainly over-
d is the number of topics in our LDA model. segment radio programs as many radio programs are longer
than one hour. However, given that our distance measure
4.1 Measuring Distance compares fixed length song sequences across playlists, this
To both manage and measure the distance between these over-segmentation should produce only minimal distortion
li × d dimensional vectors we use audioDB6 . The use of in our results. The Radio Paradise logs include all the links
audioDB to match vectors of this type is detailed in [32]. or breaks between songs where the presenter speaks briefly.
Briefly, distance is calculated by means of a multidimen- For experiments using the Radio Paradise logs these links are
sional Euclidian measure. Here li is an arbitrary length sub- used as playlist boundaries. This leads to a slight difference
sequence of i vectors. In practice, i is Casey:2008selected to in the type of playlist used from Radio Paradise versus Yes.
be less than or equal to the smallest sequence length for a 7
http://api.yes.com
6 8
source and binary available at http://omras2.doc.gold. http://www.radioparadise.com/
9
ac.uk/software/audiodb/ http://last.fm
source St Smt Pt Pavg(time) Pavg(songs)
whole set 885810 2543 70190 55min 12.62
“Rock” stations 105952 865 9414 53min 11.25
“Jazz” stations 36593 1092 3787 55min 9.66
“Radio Paradise” 195691 2246 45284 16min 4.32
Table 1: Basic statistics for both the radio log datasets. Symbols are as follows: St is the total number of
song entries found in the dataset; Smt is the total number of songs in St where tags could not be found; Pt is
total number of playlists; Pavg(time) is the average runtime of these playlists and Pavg(songs) is the mean number
of songs per playlist.
The playlists coming from Radio Paradise represent strings hypothesis, perhaps due to progaming with no reliance on
of continuously played songs, with no breaks between the time of day, at least in the case of Radio Paradise.
songs in the playlists. The playlists from Yes are approxima-
tions of a complete radio program and can therefore contain
Playlist beginning at midnight on 1 January 2007
some material inserted between songs (e.g. presenter link, 25000
commercials).
Statistics for our dataset can be see in Table 1 we then 20000
delta between start times, in seconds
use the tags clouds for these songs to estimate LDA topic
models as described in Section 310 . For all our experiments
we specify 10 topic models a priori. The five most relevant 15000
tags in each of the topics in models trained on both the rock
and jazz stations can be seen Table 2. 10000
5.2 Daily Patterns
Our first evaluation looks at the difference between the 5000
time of day a given query playlist starts and the start time
for the closest n playlists by our measure. For this evaluation 00 50 100 150 200
we looked at the 18 month log from Radio Paradise as well as result position
the “Rock” and “jazz” labelled stations from Yes.com, each
in turn. Further we used a twelve hour clock to account for
Figure 5: The time of day difference from the query
The basis for this test relies on the hypothesis that for much
playlist for 200 returned results, showing even time
commercial radio content in the United States, branding of
of day spread. Note that all the results show here
programs is based on daily repeatable of tone and content
have a distance of 0 from the query.
for a given time of day. It should therefore be expected
that playlists with similar contours would occur at similar
times of day across stations competing for similar markets
of listeners.
5.3 Inter-station vs. Intra-station
Figure 4 shows the mean across all query playlists of the In this evaluation we examined the precision and recall
time difference for each result position for the closest n re- of retrieving playlists from the same station as the query
sults, where n is 200 for the Radio Paradise set and 100 playlist. Here we looked at the “Rock” and “Jazz” labelled
for the Yes.com set. The mean time difference across all stations retrieved via the Yes API, each in turn. Similar to
three sets is basically flat, with an average time difference the first task, it is expected that a given station will have
of just below 11000 or about three hours. Given the max- its own tone or particular feel that should lead to playlists
imum difference of 12 hours, this result is entirely the op- from that station being more apt to match playlist from
posite of compelling, with the retrieved results showing no their generating station then with other stations from the
corespondance to time of day. Further investigation is re- same genre. More formally, for each query we treat returned
quired to determine whether this is a failure of the distance playlists as relevant, true positives when they come from
metric or simply an accurate portrail of the radio stations the same station as the query playlist and false positives
logs. A deeper examination of some of the Yes.com data otherwise. Based on this relevance assumption, precision
shows some evidence of the latter case. Many of the playlist and recall can be calculated using the following standard
queries exactly match (distance of 0) with the entirity of the equations.
200 returned results. Further these exact match playlists are
T
|{relevantplaylists} {retrievedplaylists}|
repeated evenly throughout the day. One of these queries is P = (2)
|{retrievedplaylists}|
shown in Figure 5. The existance of these repeating playlists
throughout the day, ensures this task will not confirm our T
|{relevantplaylists} {retrievedplaylists}|
10 R= (3)
Our topic models are created using the open source imple- |{relevantplaylists}|
mentation of LDA found in the gensim python package avail-
able at http://nlp.fi.muni.cz/projekty/gensim/ which The precision versus recall for a selection of stations’ playlists
in turn is based on Blei’s C implementation available at from both the “Rock” and “Jazz” stations are show in Figure
http://www.cs.princeton.edu/~blei/lda-c/ 6. When considering the precision and recall performance it
station label t1 t2 t3 t4 t5
Snow Patrol Bob Marley female vocalists aupa Pete 80s
rumba Feist Anna Nalick whistling new wave
“Rock” 90s john mayer Chicas Triple J Hottest 100 david bowie
green day drunk love playlist 2009 review neuentd
Dynamit feist backing vocals Sarah McLachlan fun as fuck synth pop
motown john mayer 60s Sade Flamenco
soul acoustic jazz - sax deserves another listen tactile smooth jazz
“Jazz” 70s corinne bailey rae acid jazz till you come to me guitar ponder
funk bonnie raitt reggae piano cafe mocha
Disco David Pack 2 cool jazz 2010 wine
station label t6 t7 t8 t9 t10
classic rock TRB reminds me of winter Needtobreathe Krista Brickbauer
60s ElectronicaDance kings of leon plvaronaswow2009 day end
“Rock” 70s mysterious songs that save my life The Script i bought a toothbrush
The Beatles best songs of 2009 songs to travel brilliant music bluegrass
the rolling stones tribute to george Muse van morrison omg
follow-up rnb female vocalists classic rock Smooth Jazz
jazz soul norah jones 80s saxophone
“Jazz” instrumental female vocalists dido rock smooth jazz sax
guitar Neo-Soul jazz 70s contemporary jazz
latin jazz Robin Thicke vocal jazz yacht rock instrumental
Table 2: The five most relevant tags in each topic. Upper model is all the Yes.com Rock stations, lower
model is all Yes.com Jazz stations.
is useful to compare against random chance retrieval. There cipal among these is the exploration of non-Euclidean dis-
are 100 stations labeled “Rock” and 48 labeled “Jazz”. Under tance measures. Manhattan distance (or L1 ) seems to have
chance retrieval a precision of 0.01 would be seen for “Rock” the most direct applicability and its use could prove to be
and 0.0208 for “Jazz”. quite beneficial. Another area for future work is in the use
of the measure on further data and datasets. One of the
5.4 Summary best ways to improve here would be in the use of datasets
Two different evaluation tasks have been run using real with a more exact known ground truth, in order to best ap-
world radio log data to examine the usefulness of our playlist ply known recommender and retrieval evaluation methods
match technique. The first of these, an examination the to them.
time difference was flat across result length variance. While This leads to a further avenue of future work, testing the
this implies lack of discrimination into daily patterns, it is measure against direct human evaluation. While our match-
not possible to determine from the available data whether ing technique has many uses with recommendation and dis-
this is an accurate reflection of the progamming within the covery, if it proved to align with human evaluation it would
dataset or distance measure not being sufficient for the task. be considerably more useful.
The second task shows the performance of retrieving hourly
playlists from a selection of stations using playlists from that 7. ACKNOWLEDGMENTS
station as a query. Here we see a great deal of promise,
especially when comparing the query results against random This work is supported in part by the Engineering and
chance, which it outperforms considerably. Physical Sciences Research Council via the Online Music
Recognition And Searching II (OMRAS2) project, refer-
ence number EP/E02274X/1. Additional support provided
6. CONCLUSIONS as part of the Networked Environments for Music Analysis
Having reviewed recent work in various methods of playlist (NEMA) project, funded by The Andrew W. Mellon Foun-
generation and evaluation in Section 2, it is apparent that dation. Thanks also to Paul Lamere for some dataset acqui-
there is a need for better ways to objectively compare playlists sition assistance.
to one another. We detailed a method of doing so in Section
4, though first, to better filter content-based data through
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