Wu, I.-C.; Liu, D.-R.; Chang, P.-C.: Learning dynamic information needs : a collaborative topic variation inspection approach (2009)
0.02
0.019060206 = product of:
0.03812041 = sum of:
0.03812041 = sum of:
0.01008212 = weight(_text_:a in 3293) [ClassicSimilarity], result of:
0.01008212 = score(doc=3293,freq=22.0), product of:
0.04772363 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.041389145 = queryNorm
0.21126054 = fieldWeight in 3293, product of:
4.690416 = tf(freq=22.0), with freq of:
22.0 = termFreq=22.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0390625 = fieldNorm(doc=3293)
0.028038291 = weight(_text_:22 in 3293) [ClassicSimilarity], result of:
0.028038291 = score(doc=3293,freq=2.0), product of:
0.14493774 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.041389145 = queryNorm
0.19345059 = fieldWeight in 3293, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=3293)
0.5 = coord(1/2)
- Abstract
- For projects in knowledge-intensive domains, it is crucially important that knowledge management systems are able to track and infer workers' up-to-date information needs so that task-relevant information can be delivered in a timely manner. To put a worker's dynamic information needs into perspective, we propose a topic variation inspection model to facilitate the application of an implicit relevance feedback (IRF) algorithm and collaborative filtering in user modeling. The model analyzes variations in a worker's task-needs for a topic (i.e., personal topic needs) over time, monitors changes in the topics of collaborative actors, and then adjusts the worker's profile accordingly. We conducted a number of experiments to evaluate the efficacy of the model in terms of precision, recall, and F-measure. The results suggest that the proposed collaborative topic variation inspection approach can substantially improve the performance of a basic profiling method adapted from the classical RF algorithm. It can also improve the accuracy of other methods when a worker's information needs are vague or evolving, i.e., when there is a high degree of variation in the worker's topic-needs. Our findings have implications for the design of an effective collaborative information filtering and retrieval model, which is crucial for reusing an organization's knowledge assets effectively.
- Date
- 2. 2.2010 19:22:51
- Type
- a