Friday, February 09, 2007

PAWS Meeting 2007-02-09

1st talk: Michael Yudelson
Topic: Recommenders for Information Seeking Tasks: Lessons Learned.
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”
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:
Bayes - generated similar recommendations for all users and PLSI - generated random, “illogical” recommendation

very good papers for everyone.

2nd talk: Jae-wook Ahn
Topic: A Novel Visualization Model for Web Search Results.
-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.

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?


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