PAWS Meeting 2007-02-09

1st talk: Michael Yudelson
Topic: Recommenders for Information Seeking Tasks: Lessons Learned.
Summary:

o

Goal of the recommender is to meet user specific needs with respect to:Correctness ,Saliency,Trust,Expectations and Usefulness

General Advice :

o1.Support multiple information seeking tasks

2.User-centered design nShift focus from system and algorithm to potentially repeated interactions of a user with a system
3.Recommend nNot what is “relevant”, But what is “relevant for info seeking task X”
4.Choice of the recommender algorithm: Saliency (the emotional reaction a user has to a recommendation) , Spread (the diversity of items) , Adaptability (how a recommender changes as a user changes) , Risk (recommending items based on confidence)

experiment results found out:

n

Bayes - generated similar recommendations for all users and PLSI - generated random, “illogical” recommendation

Discussion:
very good papers for everyone.

2nd talk: Jae-wook Ahn
Topic: A Novel Visualization Model for Web Search Results.
Summary:
-what's missing with current approaches? semantic relations/views, degree of relevance in terms of subjects of interest
-speed and subject of interests are new in this research.
-the system list of keywords for user to manipulate the their interests. weights are adjustable.
-WebSearchViz system, will release the opensource soon. so far, not so much detail demostration on their web.

Discussion:
how does the rotation really work?
profile the keywords has been done long time ago, it's been moved to profile the concepts

universal portal for centralized login discussion:
benefit from each system's user model.
Chirayu talked about the RBAC backend role based server as an option.
how to use existing technology to integrate all these?