Organizing . BarCampZurich, 28 Oct 2006 Gabor Cselle,

Transcription

1 Organizing BarCampZurich, 28 Oct 2006 Gabor Cselle,

2 Overview We focus on the problem of sensibly structuring s in the inbox. 1. Problem: Overload 2. Existing Approaches 3. Topic-Based BuzzTrack 4. BuzzTrack Demo 5. BuzzTrack Architecture 6. Future Work 2

3 Overload clients were not designed to handle volume and variety of messages users are dealing with today: Large volumes of Task Management Personal Archiving or Filing Keeping Context 3

4 Existing Approaches Today's Clients Visualizations Conversations Exploring the Past Analyzing Contacts Organization Schemes Automatic Foldering People-Centered Clients Task-based 4

5 Today's Clients: The Three-Pane View 5

6 Today's Clients Source: User agent strings / domain names in author's . 6

7 Visualizations 7

8 Visualizations: Conversations Gmail (Google) 8

9 Visualizations: Conversations Conversation Thumbnails root message in conversation block size = message length block color = metadata Thread Arcs Normal State root message Message 2 Selected last message time block indentation = reply structure time 9

10 Visualizations: Exploring the Past Th large, background: yearly words small, foreground: monthly words qssi nokia print btnodes items analysis master please housewarming philipp mail revamped performance analysis domain figures gross fabian team hear organization invitation amazon chatting pounce platit logo downloads miete google analyis song schemes Jan Feb Mar 10

11 Visualizations: Analyzing Contacts SNARF Correspondent Crowds Contact list sorted by Metric 1 Metric 1 (e.g. # s sent to person) Bob 42 3 Ed 35 1 Alice s sent to person circle size: total mail volume circle color: recency of last contact Metric 2 (e.g. #unread mails from) s received from person 11

12 Organization Schemes 12

13 Automatic Foldering Using machine learning techniques to automatically move s into folders upon arrival Low accuracy rates, conceptual problems: Users need to manually create folders and seed them with data Proprietary Corpus farmer-d kaminski-d accuracy Bayes RIPPER Sender 13

14 People-Centered Clients Bifrost ContactMap 14

15 Task-based Example: TaskMaster Example: TaskMaster (PARC [2]) thrasks thrask contents item contents ( s, documents, etc.) 15

16 BuzzTrack 16

17 BuzzTrack Mozilla Thunderbird extension to automatically group related s into topics. Will be distributed through website: Topics are groups of s that relate to the same idea, action, event, task, or question. We focus on organizing the user's inbox only. Paradigm shift: Fast full-text search is today's solution to finding s in archives 17

18 BuzzTrack: Clustering Methods Single-link clustering: Newly incoming s are compared to every in existing topics: Similarity value > threshold: assigned to topic Similarity value <= threshold: starts new topic Topic 1 Topic 2 new Topic 3 18

19 BuzzTrack: Clustering Features How do we generate similarity values between s? Via a linear combination of several similarity features. Examples: Text similarity (TFIDF Value, cosine similarity metric) People similarities (comparing sets of people in the From / To / Cc lines of headers) Thread membership 19

20 BuzzTrack: Clustering Features Other features for deriving similarities: Subject similarity Sender domain overlaps Sender rank and percentage % of from sender that is answered Time passed since last in topic People and reference count for Known people and reference % Cluster size Has attachment 20

21 BuzzTrack: Decision Score Similarities are combined into a decision score for each / cluster pair through a linear combination of feature values: dec i,j = w a *sim a (mi,cj) + w b *sim b (mi,cj) + Weights derived by manual trial-and-error, linear SVM on a development set of s. 21

22 BuzzTrack: Clustering Results How do we evaluate clustering quality? Topic Detection and Tracking competitions by National Institute of Standards and Technology (NIST) [3,4]. Aimed at clustering news articles. We created a corpus from 2403 own messages. Clustering Task is split into subtasks: New Topic Detection (NTD): Given stream of s, which ones start new topics? Topic Tracking (TT): Given a fixed topic, which newly incoming s belong to it? DET Curves plot miss rate vs. false alarm rate for possible threshold for decision scores det(m i,c j ) 22

23 BuzzTrack: Clustering Results TDT New Topic Detection Task Miss: 3% False alarm: 30% better better 23

24 BuzzTrack: Clustering Results TDT Topic Tracking Task Miss: 8% False alarm: 2% better better 24

25 BuzzTrack Alpha Demo 25

26 BuzzTrack Architecture BuzzTrack Plugin Architecture. Mozilla Thunderbird Python Process BuzzTrack Extension buzztrackcomm.js buzztrack.js Message Types: ClusterNew UpdateClusters GetState port BuzzTrackServer TDT buzztrackview.js Incremental Store 26

27 Future Work Distribute software, manual evaluations. Hierarchical clustering? More advanced visualizations of similarity Interactive reclustering, different clustering styles Hope BuzzTrack will be valuable tool for real-world users 27

28 Questions? References: Discussion: What do you need in an clients? What functionality would you like to see? How could you use to work better? Can we solve overload via software, or only through social customs? For grouped views such as BuzzTracks, what are the main grouping criteria? What are the features of important s? Contact: Gabor Cselle, Thanks to: Keno Albrecht, Roger Wattenhofer! 28