Widyantoro, D.H.; Ioerger, T.R.; Yen, J.: Learning user Interest dynamics with a three-descriptor representation (2001)
0.01
0.013269257 = product of:
0.053077027 = sum of:
0.053077027 = weight(_text_:et in 5185) [ClassicSimilarity], result of:
0.053077027 = score(doc=5185,freq=2.0), product of:
0.20477319 = queryWeight, product of:
4.692005 = idf(docFreq=1101, maxDocs=44218)
0.043643 = queryNorm
0.2591991 = fieldWeight in 5185, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.692005 = idf(docFreq=1101, maxDocs=44218)
0.0390625 = fieldNorm(doc=5185)
0.25 = coord(1/4)
- Abstract
- The use of documents ranked high by user feedback to profile user interests is commonly done with Rocchio's `s algorithm which uses a single list of attribute value pairs called a descriptor to carry term value weights for an individual. Negative feed back on old preferences or positive feedback on new preferences adjusts the descriptor at a fixed, predetermined, and often slow pace. Widyantoro, et alia, suggest a three descriptor model which adds two short term interest descriptors, one each for positive and negative feedback. User short term interest in a particular document is computed by subtracting the similarity measure with the negative descriptor from the similarity measure with the positive descriptor. Using a constant to represent the desired impact of long and short term interests these values may be summed for a single interest value. Using the Reuters 21578 1.0 test collection split into training and test sets, topics with at least 100 documents in a tight cluster were chosen. The TDR handles change well showing better recovery speed and accuracy than the single descriptor model. The nearest neighbor update strategy appears to keep the category concept relatively consistent when multiple TDRs are used.
