1. User Interfaces and Information Retrieval Dina Reitmeyer WIRED (i385d) 10.14.04

2. Presentation Outline Past problems with interfaces & IR Why we need good systems & interfaces How to make things better: –User-friendly systems (using statistical ranking) – D. Harman –WebMate – L. Chen & K. Sycara –Dynamic queries – C. Williamson & B. Shneiderman –Relevance profiling within documents – D. Harper, S. Coulthard, & S. Yixing Whatever happened to … ? Conclusion

3. Interfaces & IR systems: Problems Past emphasis on a working system (not on a user-friendly system) Usually tacked on a front-end for users (not necessarily user-friendly) Users without training were out of luck Interfaces using Boolean queries were common

4. Why do we need a good system & a good interface? The user of the system is “the customer”! If your system doesn’t work well, no one will use it! If your interface doesn’t work or is too confusing/difficult to interact with, no one will use it! If you want your system to be useful, how the user views it (and its interface) must be a major concern.

5. “User-Friendly Systems Instead of User-Friendly Front-Ends” –D. Harman Problem: systems not built for users (even with a front-end, still hard to use well) Need for user-friendly systems 1 Solution: use systems with statistical ranking simple definition: a system compares a document & a query and estimates the likelihood of the document’s relevance to the query

6. 4 Prototypes of Statistical Retrieval Systems PRISE - uses weighting formula, adds weights of matching terms, results are ranked CITE - similar ranking system to PRISE, uses relevance feedback* MUSCAT - different weighting system, but does use ranking, relevance feedback News Retrieval Tool (NRT) - user can use slider to weight a term, relevance feedback

7. Harman’s Results Users often preferred this type of natural language search to Boolean Searches were comparable to Boolean in speed & retrieval of relevant documents Easier for novice users; little training required The Future of Statistical Ranking: -add more complex term weighting -use IDF (measures scarcity of terms) -use relevance feedback

8. WebMate: A Personal Agent for Browsing & Searching L. Chen & K. Sycara What is it? How does it work? –A stand-alone proxy between the user’s browser & the web –Monitors user’s actions & updates itself constantly with this info. –Uses TF-IDF (term frequency-inverted document frequency) with multiple vector representations to see if a document is relevant based on what you have already deemed relevant – Uses trigger pairs model to automatically provide more search terms based on the one(s) the user provides

10. What does WebMate do? It can compile a personal “newspaper” by: –Parsing user-designated URLs –Extracting links from each headline –Fetching the pages from those links –Analyzing TF-IDF vectors for each page –Comparing a page’s similarity with the user’s current profile Results: 50-60% accuracy in articles returned It can refine a search by: –Taking the user’s search term. –Using the trigger pairs model to find related terms –Searching using those terms together Results: WebMate gives many more relevant results than if you simply type the word into AltaVista or Lycos.

11. The Dynamic HomeFinder: Evaluating Dynamic Queries in a Real-Estate Information Exploration System C. Williamson & B. Shneiderman Direct Manipulation –Benefits + graphic representation of objects/actions + buttons/sliders instead of query syntax + rapid & reversible operations (immediate results) + permits use with little/no training

13. The Experiment Users were asked to find answers to different types of queries using: –HomeFinder (a dynamic query interface) –Q&A (a natural language query interface) –Paper listings sorted by different fields

14. The Results HomeFinder rocked the house because: –Its use took little/no training. –There were no error messages. –It was good for viewing trends. –It took less time. –It was easy for novices to use. –No complex query formulation was necessary. –People just liked it better!

15. A Language Modeling Approach to Relevance Profiling for Document Browsing D. Harper, S. Coulthard, & S. Yixing With longer documents available online, users need a tool for within-document retrieval. SmartSkim: creates a relevance profile for a query about a document using language modeling (a statistical model of text that captures the distribution of text features)

16. Why SmartSkim could be cool: It highlights all query terms in the text It shows a histogram depicting the places in the text where relevant passages are to be found It is color coded to show where you have & have not looked

17. Whatever Happened to…? WebMate Dynamic HomeFinder Ben Shneiderman - SpotfireSpotfire

18. Conclusion If we want our users to utilize our information retrieval systems, we have to have both user-friendly systems & user- friendly interfaces! Tools like statistical ranking, personal software agents, relevance profiling, & dynamic queries help us provide what the user needs to interact with a system.