Reordering Search Results to Support Learning Although many learning activities involve search engines, their ranking criteria are focused on providing factual rather than procedural information. In the context of Searching as Learning, providing factual information may not be the best approach. In this paper, we discuss the relevance criteria according to traditional learning theories to support search engine results reordering based on content suitability to learning purposes. We proceeded on the investigation by selecting some selfproclaimed search literacy experts to answer thoroughly questions about their views on the reordered results. We take into account that literacy expertxe2x80x99s judgment may reveal issues regarded to technical side on learning supported by search tools. Experienced users claimed a preference for reliable sources and direct answers to what they are looking for, as they have exploratory skills to overcome information incompleteness.

Eliminating Search Intent Bias in Learning to Rank Clickthrough data has proven to be a valuable resource for improving searchranking quality. Search engines can easily collect click data, but biases introduced in the data can make it difficult to use the data effectively. In order to measure the effects of biases, many click models have been proposed in the literature. However, none of the models can explain the observation that users with different search intent (e.g., informational, navigational, etc.) have different click behaviors. In this paper, we study how differences in user search intent can influence click activities and determined that there exists a bias between user search intent and the relevance of the document relevance. Based on this observation, we propose a search intent bias hypothesis that can be applied to most existing click models to improve their ability to learn unbiased relevance. Experimental results demonstrate that after adopting the search intent hypothesis, click models can better interpret user clicks and substantially improve retrieval performance.