Hello, my name is Will Brackenbury,
and I’m a PhD student at the University of Chicago.
While cloud storage, such as Google Drive or Dropbox,
is helpful for offering additional storage capacity
for personal file collections,
many times, organizational challenges mean such collections
end up looking like this.
While researchers have developed many file management tools
to aid this difficulty, these interfaces
either only recommend file retrieval actions,
only address future disorganization,
or use rudimentary heuristics.
Through two online user studies,
we built and evaluated KondoCloud,
an enhanced file management browser for cloud storage,
that, like celebrity organizer Marie Kondo, helps reduce clutter.
It offers machine-learning based recommendations
of file retrieval, movement, and deletion actions
based on the idea that users
will want to manage similar files in similar ways.
To describe our work, I’ll start with our methodology.
We conducted two online user studies: the Observation study,
aimed at studying participants’ organizational actions,
and the Evaluation study, to evaluate KondoCloud.
Given that the methodology for the two studies overlapped significantly,
I will describe them together.
Each study was conducted in two parts. In part 1,
we recruited crowdworkers with Google Drive accounts
and asked them to complete a short survey
about their demographics and cloud storage usage.
At this time, we also asked participants to allow our web application
access to scan their Google Drive
and extract some file metadata and contents.
After a short delay, we invited back eligible participants for Part 2.
In Part 2, we asked participants to spend 30 minutes organizing
their Google Drive in the interface we provided for them,
and then answer a short series of questions relating to the organizational task.
The primary difference between our two studies’ methodologies was in this organizational task.
In the Observation study, the interface given to participants was a
bare-bones direct adaptation of an open-source web-based file browser.
In the Evaluation study, participants were assigned uniformly at random
to conditions in which they either used
the full KondoCloud interface with recommendations,
or used a modified KondoCloud interface without recommendations.
We now discuss the results from the Observation study.
For our Observation study, we recruited 69 participants
with a median of 417 files in their cloud accounts.
The most common file type was images, with “jpg” and “png”
as the most common extensions.
Participants took 5,005 file management actions in total, most of them,
66.2%, being file moves,
16.6% deletions, 13.1% folder creations,
and the remainder file or folder renames.
As seen here, however, both the number and the distribution of file actions
differed greatly by participant.
The x-axis refers to the 69 participants,
and the 2 y-axes refer to the count of actions overall,
and the distribution of actions, normalized across participants.
We see, as expected, that most participants, such as this one,
performed primarily file or folder movements,
but some participants, such as this other,
performed primarily deletions,
and a small number of participants primarily performed folder creation of renames.
This indicates that the distribution of types of actions differed for each participant.
We also see that, for file or folder movements,
these actions typically occurred in groups.
On this heat map, we are looking at pairs of actions.
The y-axis is the first action in a pair,
and the x-axis is the second action in a pair.
The darker squares for open-open, move-move, and create folder-move,
show that these actions went together relatively more often than others.
This means it was more common, for example,
for participants to move several files or folders one after another in succession,
while this was less common for deletions.
Notably, though, when one again normalizes time steps on the x-axis,
and takes the average number of actions across participants on the y-axis,
we see that these groupings can occur at any time during the organizational process.
This is because there is no significant overall “slope”
to any of the lines: if, for example,
participants tended to perform more moves early in their organization,
and fewer later, there would be a downward slope to the line.
As it is, though participants favored some action types over others,
the timing of when these occurred differed by participant.
This leads to our design of KondoCloud.
We show here the interface itself.
In the portion on the right, we see a standard file-and-folders-based browser,
in which we attempted to re-create the functionality of similar, highly-used file browsers.
where recommendations for file movement, deletion, and retrieval are surfaced.
as users organize in the right-hand browser.
When a user moves, deletes, or previews a file in the browser,
we use a logistic regression classifier to compare against other files
in the account based on 10 content and metadata similarity metrics.
We then offer recommendations to the user to perform
the same action on any files deemed sufficiently similar.
This functionality is moderated in several ways,
both generally based on design best practice,
and based on the results of the Observation study.
In general, recommendations are easily dismissed with a single click,
and do not recur once they are rejected.
They are also “consistent”, meaning that they
do not recommend more than one action for a single item at a time,
and they are “polite”, meaning that no other recommendation will be provided
for an item for a short period after a recommendation is dismissed.
In the Observation study, we saw that users
may differ in their preferred actions, and once they switch action types,
they may be more likely to stick with that action type.
Correspondingly, we modify the classifier threshold
to show more recommendations of the same type that users seem to pick more often,
and we more quickly remove from display
recommendations that do not match the current action type a user is performing.
Once we built KondoCloud, we then evaluated it.
In our Evaluation study, we recruited 59 participants,
36 in the With Recommendations condition, and 23 in the No Recommendations condition.
For participants in the With Recommendations condition,
we show along the x-axis each of the 36 participants,
and on the bottom display the disposition of the recommendations
along with the number of recommendations they received along the top.
We segment participants out into groups
based on the percent of recommendations they accepted.
We see that roughly half of participants accepted at least 10% of recommendations,
and 6 participants accepted nearly all recommendations.
This worked out to an overall 25.5% accepted recommendation rate.
We also see that many participants “manually completed” a recommended action
without using the recommendation pane while the recommendation was active.
This could indicate they did not see the recommendation,
or preferred to organize manually.
This means that overall, 36.2% of recommended actions were completed by the participants.
For participants in the Without Recommendations condition,
though they were not shown recommendations,
we tracked what recommendations they would have been shown.
Participants manually completed 32.0% of the move actions,
and 37.4% of the delete actions that would have been recommended to them.
This roughly matches the overall completion rate in the With Recommendations condition.
This suggests that the recommendations could have been valuable for these participants.
We also see that Delete recommendations may have helped participants find files
they wanted to delete in other folders.
We show here a table that has, along the x-axis, the tree distance,
which is the number of clicks needed to navigate from the originally deleted file
to the file that was subsequently recommended to be deleted,
and along the y-axis, the disposition of that recommendation.
We see that a noticeable portion of the Accepted delete recommendations
occur at tree distance 1,
meaning that the file the participant accepted a recommendation to delete
was in a neighboring folder.
This suggests that in a cluttered file tree,
it’s possible KondoCloud could help users find files to delete
they would have potentially overlooked.
Despite this modest success, we believe there are several ways in which to improve KondoCloud.
Firstly, KondoCloud does not currently group recommendations together, though a single action
though a single action could spawn a large number of recommendations—more than could be displayed on screen.
Allowing participants to interact with these groups as a whole could improve usability.
Additionally, we need to take into account context beyond file similarity.
Such as, for example, the potential depth
to which a file would be moved by a move recommendation
Lastly, future versions could address the scalability issues.
We currently pre-compute the similarity between files,
while future work could use techniques such as quantization
or learned hashing to address these issues.
Thank you very much for your time, and I look forward to your questions.