Toward Finding Valuable Topics

Enterprises depend on their information workers finding valuable information to be productive. However, existing enterprise search and recommendation systems can exploit few studies on the correlation between information content and information workers' productivity. In this paper, we combine content, social network and revenue analysis to identify computational metrics for finding valuable information content in people's electronic communications within a large-scale enterprise. Specifically, we focus on two questions: (1) how are the topics extracted from such content correlate with information workers' performance? and (2) how to find valuable topics with potentially high impact on employee performance? For the first question, we associate the topics with the corresponding workers' productivity measured by the revenue they generate. This allows us to evaluate the topics' influence on productivity. We further verify that the derived topic values are consistent with human assessor subjective evaluation. For the second question, we identify and evaluate a set of significant factors including both content and social network factors. In particular, the social network factors are better in filtering out low-value topics, while content factors are more effective in selecting a few top high-value topics. In addition, we demonstrate that a Support Vector regression model that combines the factors can already effectively find valuable topics. We believe that our results provide significant insights towards scientific advances to find valuable information.

By: Zhen Wen; Ching-Yung Lin

Published in: RC24975 in 2010


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