인지구조기반 마이닝

2006. 11. 7

소프트컴퓨팅 연구실 박사 2 학기박 한 샘

2006 지식기반시스템 응용

Learning Predictive Models of Memory Landmarks

E. Horvitz, S. Dumais, and P. Koch, 26th Annual Meeting of Cognitive Science Society, Chicago, 2004

Episodic memory

Memories are considered to be organized by episodes of significant events

Automated inference of memory landmark

Could provide the basis for new kinds of personalized computer applications & services

Focus of this paper

The construction, testing and application of predictive models of memory landmarks

Based on events drawn from users’ online calendars

Introduction

Calendar event crawlerWorks with the MS Outlook messaging and appointment management system & MS Active Directory Service

Extracts approximately 30 properties for each event

PropertiesFrom Outlook

Time of day, day of week, event duration, subject, location, organizer, number of invitees, relationships between the user and invitees, the role of the user, response status, recurrent, inviting email alias …

From Active Directory Service

(attendees) organizational peers, managers, managers of the user’s manager …

Rare contexts Atypical attendee, atypical location, atypical duration …

Events

5 participants are asked toReview all the appointments, holidays and other annotations in the calendars

Identify the subset of memory landmarks

Predictive models of memory landmarks Constructed using BN learning methods (Chickering et al.)

Data partitioningTraining : test = 80 : 20

Building Models: Data

BN structure from S1 Key influencing variables

Subject, location string, meeting sender, meeting organizer, attendees, and recurrent

Landmark eventsAtypically long durations, non-recurrence of events, a user flagging a meeting as busy

Out of office and atypical locations

Special locations

Building Models: BN Structure

Classification accuracies

ROC curves Show the relationship of false negatives and false positives for 5 subjects

Classification Accuracy & ROC Curve

As a prototypeDemonstrates how the predictive models might be used

Focuses on providing users with a timeline of landmark events to assist them to find content across their computer store

Predictive modelAllows users to train models on a portion of events from their calendar

Constructed model predicts each event if it is a landmark

MemoryLens: Characteristics

MemoryLens: Screen Shot

Memory landmarks

By threshold

Summary

This paper

Construct predictive models of memory landmarks

Provided a prototype application

Future research

Generalization of models

Beyond calendar events

New classes of evocative features

Learning models of forgetting

Summary & Future Research

M. Ringel, E. Cutrell, S. Dumais, and E. Horvitz, Proceedings of Interact 2003: Ninth International Conference on Human-Co

mputer Interaction, Zurich, 2003.

Milestones in Time: The Value of Landmarks in Retrieving Information

from Personal Stores

SearchingPeople employ various strategies when searching personal e-mails, files, or web bookmarks

Though exact dates may not be remembered, people recall the relative times of important events in their lives

SIS (Stuff I’ve Seen)Provides timeline-based presentation of search results

Provides results represented by public and personal landmark events

Indexes the full text and metadata of all the documents, web pages and email that a user has seen

Introduction

Provides an interactive visualization of SIS results

Visualization Interface

date & landmark

overview timeline

backbone

Public landmarksDrawn from events that users typically be aware of

All public landmarks have given priorities

In this prototype, all users saw the same public landmarks

HolidaysUS holidays occurred from 1994 - 2004

Priorities are manually assigned based on American culture

News headlinesNews headlines from 1994 - 2001 are extracted from the world history timeline from MS Encarta, a multimedia encyclopedia

10 MS employees rate a set of news headlines on a scale of 1 - 10

Public Landmarks

Personal landmarksThese are unique for each user

In this prototype, all landmarks are automatically generated

Calendar appointmentsDates, times, and titles of appointments stored in MS Outlook calendar were automatically extracted as personal landmarks

Each appointment has priority according to heuristics

Digital photographsCrawled the users’ digital photographs

The first photo of the day is selected as a landmark for that day

Similarly, the first one of the month and year also have high priority

Personal Landmarks

12 MS employees (male, 25-60) participated Each participant completed a series of tasks using 2 interfaces All subjects performed the same 30 search tasks After completing all tasks, subjects filled out a second questionnaire

User Study

Median search time comparisonNeutralize skewing

The difference is significant (p<0.05)

Result: Search Time

7-point scale (1: strongly disagree, 7: strongly agree)

Result: Questionnaire

ConclusionsA timeline-based visualization of search results

An interface with public and personal landmark events aid people in locating the target of their search

A user study found there was a significant time savings for searching

Future workExtending the type of events (personal & public, now)

Refining heuristics in selecting and ranking landmarks

Conclusions & Future Work