Personal Information Management Vitor R. Carvalho : Personalized Information Retrieval Carnegie Mellon University February 8 th 2005

Motivation 1 person → several tasks Several contexts Several past activities Several collaborators Several future plans More and more personal information stored Where’s that document ??? Where’s the link to that blue hotel in New York ?

Document Types Some Commercial (Partial) Solutions Web Links Passwords Calendar Text, PDF, ZIP, PS, Latex, RTF, DOC, XML, XLS, PPT, etc IM Audio Video Research: retrieval techniques, prototypes, evaluation, HCI, how users access old documents, visualization, etc.

1 st System: Haystack From 1997-now, MIT. Comprehensive system to personalize IR and relationship between a particular individual and his corpus. Agnostic regarding the particular search tool used. Augment the power of search tools by personalizing and improving the representation of the data recorded. Uses very general data structure. Supports different annotations and different collections. Work with information, not programs. +IM+todoList+calendar+webbrowser+photos+etc together. Indexing is done incrementally. During “calm” periods.

Haystack Architecture

3 ways to harvest data: Data Driven: docs already in Haystack (deletion, selection, etc) or new docs added by user Observers: observing user’s moves (browsing, searching, saving queries, etc) Human annotation: via an special interface Hard to evaluate in large studies You can download the first versions of Haystack from New Eclipse-based Semantic Web Browser (Based on Haystack)

2 nd System: KFTF (Keeping Found Things Found) User study and a survey on how individuals keep and organize info they’ve found on the web. (and want to re-access and reuse it) 24/214 participants: researchers, managers and information professionals. Figure 2: Top 7 keeping methods as ranked by proportion of participants using the method at least once a week

3 rd System: Stuff I’ve Seen (SIS) 2003, Microsoft. Design and evaluation (user study) of a system to “Find things you have seen before” 58-81% of webpages are re-visits. Unix commands, library borrowing, human memory, etc…likewise. Main ideas: 1.Unified index of information across different info sources (calendar, web, , files, etc) 2.Rich contextual cues to trigger memory (author, time, thumbnails, etc). 3.Friendly interface that allows quick feedback and iterative refinement

Stuff I’ve Seen

SIS - Evaluation Supports Boolean as well as best match (Okapi’ probabilistic ranking alg.) retrieval on text and metadata properties. Allows phrases, wildcards and proximity search. 234 people during 6 weeks. Only 7.5% used boolean operators, or phrases in query Queries were short (1.59 words) –- the web ~ 2.35 words Personal datasets from 5K to 100K items Most used filters: file type and date range. Most common query types: People’s names File types opened: s(76%), web(14%), files(14%) Standard ranking functions seem less important in this context

SIS - Evaluation Similar power functions found in webpage re-access and memory re-access Overall, system had a very good acceptance

4 th System: Using Temporal Landmarks 2003, Microsoft. Based on the “Stuff I’ve Seen” system. Synthesis of 2 Ideas: –Epsodic Memory – use landmarks in user’s memory as cues to retrieve information (JFK assassination, 9-11, unforgettable Steelers game, vacations, etc) –Timeline Visualizations – visualize personal dataset in sequential time

Selection of Public Landmarks: priority of important holidays, analysis of news headlines, etc. User driven approach. Selection of Personal Landmarks: different priorities to calendar appointments, “out of office” times, recurrent appointments have low priority, digital photographs (first photo of the day was selected) Temporal Landmarks - Evaluation

Some Questions 1.I was wondering what people think about using a whole personalized web search system with/as a query observer in the haystack system. This might be interesting if the system had access to the haystack internal data and could write back to it. 2.General data model of Haystack approach is quite similar to knowledge map approach. Even though they applied their specific need into these kinds of semantic network, the paper missed semantic network retrieval model. Are there any papers that allow us to retrieval these semantic network?