This paper presents a statistical framework based on Principal Component Analysis (PCA) for discovering the contextual factors which most strongly in\ufb02uence user behavior during information-seeking activities. We focus particular attention on explaining how PCA can be used to assist in the discovery of contextual factors. As a demonstration of the utility of PCA, we employ it in an Implicit Relevance Feedback (IRF) algorithm that observes features of user interaction, computes the feature co-variances from a few seen documents, and calculates the eigenvectors of the co-variance matrix to be used as the basis for ranking the unseen documents. This ranking is then compared with the ideal ranking that could be computed if the ratings explicitly given by the user were known. The most e\ufb00ective eigenvector, in terms of impact on retrieval performance, was chosen as representative of each user\u2019s intent. Our experiments showed that each aspect of user behavior is in\ufb02uenced by di\ufb00erent contextual factors, yet there exist some important features common to most factors. Our \ufb01ndings demonstrate both the e\ufb00ectiveness of the IRF algorithm and the potential value of incorporating multiple sources of interaction evidence in their development. In particular, it was shown that IRF was more e\ufb00ective when the eigenvectors are personalized to each user.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper presents a statistical framework based on Principal Component Analysis (PCA) for discovering the contextual factors which most strongly in\ufb02uence user behavior during information-seeking activities. We focus particular attention on explaining how PCA can be used to assist in the discovery of contextual factors. 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