Information retrieval evaluation based on the pooling method is inherently biased against systems that did not contribute to the pool of judged documents. This may distort the results obtained about the relative quality of the systems evaluated and thus lead to incorrect conclusions about the performance of a particular ranking technique.

For enterprise search, there exists a relationship between work task and document type that can be used to refine search results. In this poster, we adapt the popular Okapi BM25 scoring function to weight term frequency based on the relevance of a document type to a work task. Also, we use click frequency for each task-type pair to estimate a realistic weight. Using the W3C collection from the TREC Enterprise track for evaluations, our approach leads to significant improvements on search precision.

Professionals in the workplace need high-precision search tools capable of retrieving information that is useful and appropriate to the task at hand. One approach to identifying content, which is not only relevant but also useful, is to make use of the task context of the search. We present X-Site, an enterprise search engine for the software engineering domain that exploits relationships between user's tasks and document genres in the collection to improve retrieval precision.