THE SONGS OF OUR PAST DOMINIKUS BAUR WORKING WITH LISTENING HISTORIES UNIVERSITY OF MUNICH (LMU) GERMANYHi, I’m Dominikus from the University of Munich. I’m a fourth year Ph.D. student and today I will talk about some of the work that I’ve done so far.
In this talk:As you’ve probably already guessed from the Btle, my focus is on a special type of personal histories, namely music listening histories. In this talk I will ﬁrst describe what listening histories are and what we mean by that. Then I’ll show you some projects from the area of informaBon visualizaBon where we worked with listening histories from single or mulBple people and tried to make them understandable for them. Finally I will give you some ideas what else than visualizing we could do with this type of data.
Photos, be they analog or digital, are a common way to remember the past. We all take photos while on vacaBon, having friends over or for all these other occasions and aIerwards look at them (or don’t) and think about the past. But what we do in our lives is oIenBmes so much more than a photo can capture
[click] unfortunately all auditory informaBon is lost in the process. The music we made, the songs we heard. Nowadays, of course, there is oIen a structural diﬀerence between both acBviBes: The Bme we spend acBvely making music is signiﬁcantly smaller than the Bme we spend listening to it.
But even though we no longer make the music, it is more abundant than ever before thanks to our mobile gadgets. And so these songs by people we don’t know sBll stand for parts of our lives:
Or the one that was playing when you met a special someone…
… and of course the songs we hear every year for special occasions.
REMINISCINGSo, an account of all the music we listened to, a listening history, can serve for reminiscing just as well as photos. In this regard, listening histories are a part of the so-‐called lifelogging data.
Lifelog A digital representation of all aspects of one’s lifeLifelogs are digital representaBons of aspects of one’s life. So, via this deﬁniBon, every facebook status and blog entry already stands as a part of lifelog data. But the original vision of lifelogging consists of capturing really everything that you experience. And the original visionaries went …
… to great lengths to reach that goal. So while capturing listening histories is only a humble secBon of a complete lifelog, they can sBll bring many of the beneﬁts.
REMINISCING Sellen, Whittaker: Beyond Total Capture: A Constructive Critique of Lifelogging, CACM, May 2010In a recent paper, Abigail Sellen and Steve WhiXaker idenBﬁed some of the beneﬁts that lifelogging data can bring and summarized them as the ‘5 Rs’. We’ve already seen reminiscing, as re-‐living the past for emoBonal reasons.
RECOLLECTINGREMINISCINGRETRIEVINGREFLECTINGREMEMBERING Sellen, Whittaker: Beyond Total Capture: A Constructive Critique of Lifelogging, CACM, May 2010But there’s more.
RECOLLECTINGREMINISCINGRETRIEVINGREFLECTINGREMEMBERING Sellen, Whittaker: Beyond Total Capture: A Constructive Critique of Lifelogging, CACM, May 2010RecollecBng is the more general (and less emoBonal) case of reminiscing and can, for example, mean using a listening history to ﬁnd a song whose name I have forgoXen.
RECOLLECTINGREMINISCINGRETRIEVINGREFLECTINGREMEMBERING Sellen, Whittaker: Beyond Total Capture: A Constructive Critique of Lifelogging, CACM, May 2010Retrieving is more appropriate for text-‐ and other documents, but it can also mean that I can immediately listen to that song.
RECOLLECTINGREMINISCINGRETRIEVINGREFLECTINGREMEMBERING Sellen, Whittaker: Beyond Total Capture: A Constructive Critique of Lifelogging, CACM, May 2010ReﬂecBng describes the process of thinking about your life using the lifelog. Say, I listened to a fair share of pop and rock, now it’s Bme to become serious and listen to classical music.
RECOLLECTINGREMINISCINGRETRIEVINGREFLECTINGREMEMBERING Sellen, Whittaker: Beyond Total Capture: A Constructive Critique of Lifelogging, CACM, May 2010And ﬁnally, remembering intenBons describes thinking about prospecBve acBviBes, such as regularly checking if a band has a new album or is on tour.
RECOLLECTINGREMINISCINGRETRIEVINGREFLECTINGREMEMBERING Sellen, Whittaker: Beyond Total Capture: A Constructive Critique of Lifelogging, CACM, May 2010So, these ‘ﬁve Rs’ provide a good overview of the possible beneﬁts of capturing listening histories.
Let me talk a liXle bit about what listening histories are and where they come from.
Listening history A complete chronological collection of musical items …In my understanding an ideal listening history describes all songs that a person has listened to, possibly in their lifeBme. What’s important here is that …
... Each song: (1) pre-existing piece of music ...Each song is a pre-‐exisBng piece of music that has aXributes such as arBst, Btle, etc.
… (2) has been heard at least partiallyAnd second, each song has been heard by the owner of the history at least in parts. So, the quesBon is, where do we get such data from?
Fortunately, there’s a popular service called ‘last.fm’ that’s been around for a while and does exactly that. Last.fm’s actual intenBon for capturing a person’s listening behavior is providing beXer recommendaBons for their webradio, but the resulBng listening histories are easily accessible through their API which makes them a perfect target for all kinds of projects.
Last.fm’s tracking technology is called ‘Audioscrobbler’, which is both a protocol and a soIware. Devices and media players can either use the protocol directly or rely on the background audioscrobbler process running on the user’s machine. And in the end we arrive at a chronological list of /all/ the songs a person has listened to...
+ =But while this could be used to hypotheBcally capture the complete listening history of a person and works great in theory [click]
Real listening histories: - incomplete - noisyThe actual resulBng listening histories are both incomplete and noisy. Let me just tell you what I mean by that.
Real listening histories: - incomplete - noisyGaps in a listening history can come from various places…
One common source is that the listener is using non-‐supported hardware for listening
Another that music comes from other sources like when shopping or being at a friend’s place.
Real listening histories: - incomplete - noisyNoise, i.e., too many songs are tracked is also quite common…
The user might leave the computer while the music keeps on playing…
Or someone else is using the computer while the audioscrobbler is sBll running.
> 50%Another Caveat: The audioscrobbler only tracks a song after the user has listened to atleast half of it. Again, keep in mind that the main incentive for last.fm to track listeningis improving the recommendations of their web radio: If a song is skipped then thelistener probably didn’t like it and it’s uninteresting for recommendations.
30 million users / month (March 2009) http://blog.last.fm/2009/03/24/lastfm-radio-announcementSBll, despite these downsides, last.fm’s data is preXy reliable and the service is very popular. According to them, 30 million people visit the webpage per month.
In the end we arrive at a chronological list of songs and that’s all we get. Each secBon of Bme either contains music or it does not. So we have, for example, no informaBon on the context of the music listening (I’ll get back to that aspect later). SBll, to make it easier to understand this data we can then start to analyze it and e.g., to extract listening sessions.
Listening sessions are characterized by the gaps between the songs, so a gap of e.g., half an hour between two songs means that the creator of the history stopped listening and thus ended the session. To make these histories a bit more meaningful we can also go beyond the single Bme dimension…
Genre ……Sub-Genre Artists Albums Songs… and put the songs into the musical hierarchy of albums, arBsts and genres. While this classiﬁcaBon is not perfect and oIenBmes the topic of heated debates, at least it’s widely-‐known among all music listeners. One more step to overcome the downsides of a strict hierarchy is adding user-‐generated keywords into the mix…
Genre ……Sub-Genre Artists Albums Songs Tags… that can become a stand-‐in for any number of diﬀerent hierarchies or classiﬁcaBons. You will see some of these aspects in the prototypes that I’m about to show you.
But back to the actual beneﬁts to the creators of such listening histories, think of the ‘5 Rs’ of reminiscing, recollecBng and so on. Here you can see the default view of Last.fm presenBng this data: A chronological web-‐based list which is not that helpful for any of these tasks. And as you’ve just seen, listening histories can become quite complex once you dive into their depths which makes other forms of presentaBon more useful.
As a ﬁrst step towards understanding this data and also making their owners understand them I started building visualizaBons based on it. To make maXers not overly complicated, …
… I used only a single listening history, i.e. a possibly long list of possibly repeated songs.
My ﬁrst approach to visualizing this type of data was a node-‐link diagram: The idea was that each unique song would be represented as a node …
… while each pair of consecuBve songs would form an edge in the diagram. And while this concept was easy to understand, the result wasn’t – necessarily. And you might also understand why I Btled this visualizaBon ‘ Tangle’:
Here be a chaoBc screenshot of tangle TangleWhile it certainly looks chaoBc, there are sBll several aspects that you can draw from it:
Here be a chaoBc screenshot of tangle TangleFor one, the layout of the nodes is force-‐directed, which means that nodes with many edges (i.e. songs that appear repeatedly within the history) are drawn towards the center, …
Here be a chaoBc screenshot of tangle Tangle… while less popular songs and one-‐hit-‐wonders are on the outskirts.
Here be a chaoBc screenshot of tangle TangleAn addiBonal encoding is the thickness of the connecBng arrows that represents the number of Bmes this two-‐song-‐sequence was played which shows albums and pre-‐deﬁned playlists.
VIDEO Tangle[VIDEO] And ﬁnally, Tangle’s layout is, as I said, force-‐directed which means that the user is able to interacBvely explore the visualizaBon. Zooming and panning is of course possible. By hovering over a song addiBonal informaBon is shown. And the user can drag around songs at will.
As it was not easy to learn much from the Tangle visualizaBon, I wanted to put some sense into it. Filtering or splihng the data seemed promising, so I focused on listening sessions this Bme.
The basic idea was again a node-‐link diagram, but this Bme songs could appear more than once.
This Bme the more important factors were the Bme stamps of the songs. A pause of in this case 1 hour indicates the start of a new listening session.
StringsBy sorBng the sessions chronologically we arrived at this visualizaBon, called ‘Strings’.
StringsZooming out gives you an overview of the length of your listening sessions, shows outliers and Bmes when you didn’t listen to music. The verBcal Bme line is very important in this regard.
StringsFinally, you probably wondered about the blue-‐ish arcs: The problem with Strings is that each song can possibly appear several Bmes in the visualizaBon as single songs are no longer represented by single nodes. Therefore, we draw arcs between idenBcal songs which makes it possible to gauge the importance of one song or see repeBBve sequences (at the boXom).
?So, what these two examples had in common that they were both restricted visualizaBons that (1) focussed on one aspect of the data and (2) allowed only liXle interacBon.
playfulIf you want to put a label on them it would probably be ‘playful’ which means: They are designed for one speciﬁc aspect of the data which cannot be customized. They’re built for this task only. But they can sBll engage the user to play around and interact (at least a liXle).
playful casual expertIf you want to put this into an infovis perspecBve, two other commonly used terms are useful: ‘Casual’ describes visualizaBons that are a liXle more interacBve and customizable but not as complex as ‘expert’ systems that allow ﬁne-‐grained customizaBon but require solid knowledge in the respecBve area.
playful casual expertFor visualizaBons: A type of playful visualizaBon would be Wordle, engaging but with a single purpose. The Many Eyes project is easy to use but has much more ways to display and ﬁlter the data. Finally, programming frameworks such as protovis or processing allow utmost ﬂexibility but are diﬃcult to get into and master.
Interactivity expert casual playful ComplexityIf we’re inclined to put these three concepts into relaBon to each other, we can use interacBvity and complexity. So, playful tools aren’t very ﬂexible, but also not very complex. Expert tools however are mulB-‐purpose and highly interacBve but also diﬃcult to master. It depends on the user populaBon and the task what visualizaBon concept to choose.
Interactivity expert casual playful ComplexityIn our case with listening histories, we have people who like to listen to music and are not necessarily infovis-‐experts. Also, analyzing their listening behavior is something they don’t do regularly so forcing them to learn something for using a complex visualizaBon will rather put them oﬀ than engage them. Therefore I concentrated on the playful/casual corner of this design space.
Ok, so back to the visualizaBon. Both Strings & Tangle were very single purpose and liXle customizable. For the next project, I wanted to give users more freedom in analyzing their listening histories but sBll keep the tool accessible. Strings & Tangle were also only informally evaluated with a few people from our lab so I wanted to see if real people would actually ﬁnd something like that useful…
LastHistory... The result was LastHistory, a /casual/ infovis tool for analyzing and reminiscing in one’s own listening history. We made it available on the internet. Several thousand people downloaded it and we received lots of feedback. When designing LastHistory we ﬁrst wanted to make sure that it felt easily accessible for people. The visualizaBon in its non-‐interacBve state should already give insights to the user, and so gradually lure them into exploring the more sophisBcated opBons.
LastHistorySo, the largest part of the applicaBon is taken up with a 2D Bmeline: all songs are represented as small circles and mapped horizontally to the day and verBcally to the Bme of day of their Bmestamps. This way, users can easily see daily rhythms,
LastHistoryLike at what Bme this person usually went to bed.
LastHistoryAnd here’s another example: A user who gets up at the same Bme everyday and listens to music ﬁrst thing in the morning.
classical jazz funk hip-hop electronic rock metal unknown/other LastHistoryEach song’s genre is color-‐coded, so the user gets an immediate overview over the variety of songs. We’re of course restricted in the number of colors we can use to keep them disBnguishable.
LastHistoryBeyond staBc visualizaBon, users can navigate within the visualizaBon by panning, triggered by dragging with the mouse
LastHistoryOne-‐dimensional zooming by using the mouse’s zoom wheel or the slider in the lower right corner allows them to focus on certain secBons of the history.
LastHistoryHovering over a song shows a box with user-‐generated keywords from last.fm, but more prominently: connects this song with all other instances of it throughout the history. So, users can easily see when they listened to this one song.
LastHistoryPreceding and succeeding repeated songs are also highlighted, so sequences such as albums or other predeﬁned playlists are automaBcally highlighted.
LastHistoryFinally, in the upper right corner of the applicaBon, there’s a textbox for ﬁltering where users can enter freeform terms. It’s possible to enter song or album Btles or arBst names to ﬁlter all other songs.
LastHistoryBut the ﬁlter box can also be used for temporal queries by entering dates, or periods of Bme, so users can, for example, see all songs that they listened to in autumn before noon. But enough with the default infovis-‐features. One interesBng aspect of this project was that we could use an addiBonal data source for gaining insights: The user’s memories.
To access these, we needed memory triggers. Some research in psychology has shown that personally created things such as photos can be useful in this regard, so we integrated photos and calendar entries from the user’s harddisk.
Two usage modes: Analysis PersonalWe split these into two diﬀerent usage modes and called them ‘analysis’ (everybody can do it) and ‘personal’ (with memory triggers that probably are only useful to the owner of the history). So in this personal mode we have contextual informaBon that makes it easier to remember what happened at what Bme and understanding the listening decisions. Users could simply switch between the two modes with the buXon in the upper leI corner.
Ok, so much for the tool. As I said, we made it available on the internet and a lot of people downloaded it.
Praise on tech blogs 5,000 downloads 243 ﬁlled-out questionnairesSome numbers: First we got a good amount of coverage on tech blogs, which led to a certain popularity. Right now, we have about 5,000 downloads. We also included a link to a quesBonnaire that pops up aIer ﬁIeen minutes of using the tool and around 250 people answered that quesBonnaire. We kept that intenBonally short, in order not to put oﬀ people as a short answer is beXer than no answer at all.
About the Personal Mode: “I like this mode the best, it should be the default mode!” “Clicking on a photo gallery and listening to what I was listening to at the time was very powerful.”People who had photos and calendar entries available enjoyed using the personal mode and also features like the possibility to create a slideshow of music and photos.
About the Analysis Mode: “I rarely listen to music between the hours of 9-11 a.m., even on weekends.” “I noted the … commuting pattern.” “Those ruts where you get stuck in listening to one particular song.” “I listened to music for 4 straight days!”And in general, people were also able to ﬁnd repeaBng paXerns and liked how they were able to learn interesBng aspects about themselves.
75% found it easy to use and learn.Another thing we learned that worked really well was puhng a ﬁve minute video online that explained how to use the tool: There was no online help or something like that available and sBll 75% percent found it easy to learn and use.
Finally, people really liked to share the results. And as there was no straighporward way to do it within the applicaBon, they resorted to taking screenshots for posBng it on ﬂickr or their blogs. So a future version should deﬁnitely take that into account.
AOk, so these were three examples for visualizing single listening histories. But it gets much more interesBng when we have not one history…
E D C B A… but many more. And as music has an intricate social funcBon as well, comparing one’s taste in music to friends, family and peers can be an interesBng use case.
So in the following, I will give you two examples for approaches to visualize these data. Again, I’ll present one playful and one casual approach.
B AWhile mulBple histories can mean a lot of histories, for a ﬁrst approach, I decided to focus on just two histories. One will usually be the user’s history and the other one of a friend or another person that he or she knows.
Ok, so what’s the best way to do that: Aligning the songs to a Bme-‐line is probably a good idea, to allow comparisons for the number of songs, regularity of listening and so on. But users are especially in this one-‐on-‐one scenario interested in also comparing their taste: Are both of them listening to the same songs or arBsts? Or is there no similarity?
An easy way to encode that is using the distance between the songs from each history: The closer a song gets to the other history, the more similar it is to it, resulBng in a fever chart of relatedness.
LoomFMHere’s an example from the resulBng ‘LoomFM’ visualizaBon. You have a horizontal Bmeline and two listening histories from user red and purple. The closer one of the small song circles comes to the Bmeline, the more related it is to the other user’s taste in music.
LoomFMSome more things: The more consecuBve songs share the same genre or arBst, the larger the corresponding label gets. By doing this, important arBsts are visible even when zoomed out. Also, labels that both users share at the same point in history move to the center of the Bmeline.
LoomFMAddiBonally, the yellow arcs connect idenBcal songs – the same principle as in the ‘Strings’ visualizaBon. Using this approach, you can get a sense if a person repeatedly listens to the same songs (as user red in this example) or only once. Also, songs that both users share are connected…
LoomFM… as in this example, where a new album of ‘ Trail of Dead’ was released and both users gradually started listening to it. (you can also clearly see the sequence here)
VIDEO LoomFMHere you can see a video of LoomFM. As always, zooming and panning are possible and gehng more informaBon by hovering over songs or arcs.
playful casual expertLoomFM consBtutes an example of a playful visualizaBon for mulBple histories. The tasks are clearly deﬁned, interacBon is minimal and a lot of informaBon (e.g. the similarity between songs) is implicit and predeﬁned…
Screenshot Of LastLoopplayful casual expert… to overcome the restricBons and also to integrate more than two histories we did another project called LastLoop and aimed more for the casual area.
The basic idea was to have a cross between LastHistory and LoomFM, to give users the chance to do these more complex analyses using ﬁltering and things like that while also being able to connect the diﬀerent listening histories and see relaBons between them.
LastLoopHere’s the result that we called ‘LastLoop’. What you can see here are three listening histories (you can have an unlimited number of verBcally stacked histories), arranged to the same Bmeline.
LastLoopWe used the 2D Bmeline metaphor from LastHistory once more, so it’s possible to see daily paXerns across all histories.
LastLoopAlso, by hovering above a song, all other occurences within this one history and the others are highlighted (re-‐using the metaphor from the other projects).
LastLoopThe user can also select a whole area and again, see where else the songs appear.
LastLoopFinally, to make the informaBon manageable, users can also search for songs, arBsts, albums and so on…
VIDEO LastLoopAnd here’s the system in acBon: You can pan and zoom either by using the mouse or the Bme slider at the boXom of the screen, select screen regions to see other occurences of the selected songs (and switch between all, songs from the selecBon or songs within the selecBon only). Aaaaaand you can also highlight songs or arBsts … and ﬁlter for certain genres.
http://www.lastloop.deSo, to evaluate the system we followed the same strategy that had already worked with LastHistory. We made the tool available on the web (and this Bme it was even wriXen in Java and thus plaporm-‐independent, while LastHistory was Mac-‐only). You could -‐ and sBll can -‐ run it easily in your browser.
For learning the applicaBon, we provided another ﬁve-‐minute-‐video that explained the basics of interacBon and to capture the users’ ﬁndings we had another short quesBonnaire …
… and we also had ‘feedback’ buXon in the upper leI of the applicaBon where users could click on, provide what they found and send it directly back to us.
21 ﬁlled-out questionnaires (3 incomplete)So, while we were preXy convinced that we did everything right, the response was less than stellar. AIer one month we had 21 responses to the quesBonnaire and a few with the direct feedback buXon.
Insights gained: “That one user is also listening to a very infamous band from the 70s” “When did the other user hear my favorite song, have there been many connections lately, …”What we found was that people learned about themselves and others, which was the goal of the visualizaBon and we were happy that it worked. But we wanted to ﬁnd out what went wrong…
Selecting a song was sketchy Results were cluttered and unclear… and the problems were mostly due to usability issues and the general complexity of the applicaBon. People found it diﬃcult to accurately select a song as the selecBon was only based on the horizontal posiBon of the cursor and not the verBcal (so it became very hard to select a speciﬁc song when zoomed out). Also, people liked how the results looked but couldn’t make much sense of them. It was oIen just too much informaBon in too liXle space, so drawing any insights other than very superﬁcial ones was diﬃcult.
Screenshot Of LastLoopplayful casual expertSo what we learned was that even when we ﬁxed the usability issues, LastLoop would probably sBll be more of an expert-‐ than a casual visualizaBon.
Screenshot Of LastLoopplayful casual expertOk, now that you’ve seen 5 examples for visualizaBons of listening histories that approached diﬀerent aspects of the topic, where do we go from here?
RECOLLECTINGREMINISCINGRETRIEVINGREFLECTINGREMEMBERING Sellen, Whittaker: Beyond Total Capture: A Constructive Critique of Lifelogging, CACM, May 2010VisualizaBon is nice and all, but there is more that we can do with these histories. It’s nice to give the creators of these histories the chance to recollect, reminisce and so on, but we can also use them to make their day-‐to-‐day interacBon with music easier and more convenient.
In these last few minutes of my talk I will show you two examples of how to use this data in other areas.
One problem with listening to music is that there a mostly only two ways to do it: You either manually create a playlist or pick an album or have it done fully automaBcally. The former makes it very tedious to listen to music (especially on the go), while the laXer restricts you to the choice of the machine that might be giving you the same songs over and over again and you have very liXle inﬂuence on that.
RushWith our Rush-‐interacBon technique we wanted to create and opBon for building playlists between the two extremes and we called this approach ‘repeated recommendaBons’…
VIDEO RushYou start just like in the automaBc case with a hand-‐picked seed song and receive a set of ﬁve recommendaBons for this item. Once you choose once of these items, you get another set of ﬁve and so on and so forth. The great thing about this approach is that you do not have the large overhead of going through your whole collecBon to create a playlist, but sBll have much more freedom than in the purely automaBc case.
So where do listening histories come in here? First, we can of course use them to shape the recommended items. In our study we used a pre-‐deﬁned set of music and general recommendaBons from last.fm but it would of course make more sense to adapt the recommendaBons based on the user’s history….
… second: Five items is not a lot, so it is diﬃcult to choose the right ones in order not to frustrate the user. Having his or her listening history available means that we can automaBcally remove candidates that the user does not know (and would not be very helpful in this scenario).
RECOLLECTINGAnother thing that you can do when working with listening histories is use them for rediscovery of music that you forgot. That was something that we oIen observed when people used one of the visualizaBons that they were happy to ﬁnd some song or arBst that they had forgoXen about.
But using the visualizaBons is an explicit acBvity and people commonly use diﬀerent soIware to actually listen to music. So in this last project, we wanted to help them with recollecBng and reminiscing while they were actually listening to music.
So we decided to make a plugin for a media player. Because we wanted to keep it useful for as many people as possible we chose Songbird, an open source media player with an acBve community, that’s available for Mac and Windows instead of iTunes or the Windows Media Player.
Our idea for supporBng rediscovery was based on the idea that also the Tangle visualizaBon was based on: Every Bme a song appears in a listening history it has successors and predecessors. And this order of songs is probably important for the listener, not always, of course, but at least someBmes. So the idea was to show for the currently playing song whatever songs appeared before and aIer it.
SongSlopeThe result looks like this: By doing what they would have done anyway, namely listening to music, users automaBcally receive a focused glimpse into their listening past. All songs before and aIer are displayed and they can switch to one of these songs simply by clicking on them.
SongSlope… and users can also switch to a view of the underlying listening sessions, browse through them or listen to them as a new playlist.
Currently: 7,200 downloads 58 ﬁlled-out questionnaires (40 partial)We had a lot of downloads (as I said, Songbird has a very acBve community) but not as many answers to the quesBonnaire, probably because we had no pop-‐up or email reminder to ﬁll it out. We also logged the relevant aspects of the user’s interacBon with the plug-‐in (of course, only aIer they agreed to that).
Use cases: 44.8% Re-discovering music 31.0% Generating playlistsWe were especially interested in what people used it for and found that almost half of them were able to rediscover music with it, but also almost a third used it for creaBng playlists (or relistening to old playlists). So even though only a couple of people answered the quesBonnaire we got very posiBve feedback from them.
Ok, so where does that leave us and what can you take away from this talk:
Listening histories are today mostly used for recommendaBon. But as they are a type of personal data that can be easily collected and sBll can have a powerful impact into people’s lives using them for recommending music only is – I think – somewhat of a waste. We can do much more with them.
Screenshot Of LastLoopplayful casual expert… as you’ve seen: We can visualize this informaBon to allow people to reminisce about their past and recollect their memories, in varying degrees of complexity and for various approaches to the topic…
And beyond navel-‐gazing we can also use this data for helping people with listening to music: We can use listening histories to improve the usability on mobile devcies for quickly and conveniently creaBng personalized playlists on the go or to add value by lehng people painlessly rediscover music while listening to it anyway.
Genre ……Sub-Genre Artists Albums Songs TagsSo, for three more concrete results that I learned while working this topic: It’s probably a good idea to use a Bmeline as the central metaphor for represenBng personal histories, as the temporal aspect is very important for ﬁling this data into one’s personal life story. Also, abstracBons such as genre hierarchies are great for reducing the complexity of the data while preserving the access to single items.
131Second, for collecBng results from casual users several approaches can be helpful: We had quesBonnaires that popped up aIer a while in LastHistory, we tracked relevant interacBon with the user’s consent to learn about how an applicaBon is used and where it fails (in SongSlope) and ﬁnally, the feedback-‐buXon that we had in LastLoop allowed for impromptu feedback with minimal overhead.
Finally, one very interesBng data source that we tapped when creaBng LastHistory were the user’s memories. These memories can give context and meaning to plain lists of songs and by using suitable memory triggers it’s possible to unearth great stories and understand these histories. Depending on the use case, visualizaBon shouldn’t underesBmate the value of having a real person sihng in front of the machine.
I think the central part is that these histories are reﬂecBons of their creators’ lives: Music accompanies them during their good and their bad Bmes, their triumphs and their tragedies and forms an inseparable bond with these events. But what they are lacking are the tools to use them in the same way that they use photos for reﬂecBng about their past and making sense of their lives. So I hope my work is a ﬁrst step towards giving this data back to the people who created it.
DOMINIKUS BAUR UNIVERSITY OF dominikus.baur@iﬁ.lmu.de MUNICH (LMU), twitter: @dominikus GERMANYThank you!