Shepherd, M.; Duffy, J.F.J.; Watters, C.; Gugle, N.: ¬The role of user profiles for news filtering (2001)
0.05
0.05114883 = product of:
0.10229766 = sum of:
0.10229766 = product of:
0.20459533 = sum of:
0.20459533 = weight(_text_:news in 5585) [ClassicSimilarity], result of:
0.20459533 = score(doc=5585,freq=14.0), product of:
0.26705483 = queryWeight, product of:
5.2416887 = idf(docFreq=635, maxDocs=44218)
0.05094824 = queryNorm
0.76611733 = fieldWeight in 5585, product of:
3.7416575 = tf(freq=14.0), with freq of:
14.0 = termFreq=14.0
5.2416887 = idf(docFreq=635, maxDocs=44218)
0.0390625 = fieldNorm(doc=5585)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- Most on-line news sources are electronic versions of "ink-on-paper" newspapers. These are versions that have been filtered, from the mass of news produced each day, by an editorial board with a given community profile in mind. As readers, we choose the filter rather than choose the stories. New technology, however, provides the potential for personalized versions to be filtered automatically from this mass of news on the basis of user profiles. People read the news for many reasons: to find out "what's going on," to be knowledgeable members of a community, and because the activity itself is pleasurable. Given this, we ask the question, "How much filtering is acceptable to readers?" In this study, an evaluation of user preference for personal editions versus community editions of on-line news was performed. A personalized edition of a local newspaper was created for each subject based on an elliptical model that combined the user profile and community profile as represented by the full edition of the local newspaper. The amount of emphasis given the user profile and the community profile was varied to test the subjects' reactions to different amounts of personalized filtering. The task was simply, "read the news," rather than any subject specific information retrieval task. The results indicate that users prefer the coarse-grained community filters to fine-grained personalized filters
