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Click through events in search results pages (SERPs) are not reliable implicit indicators of document relevance. A user's task and domain knowledge are key factors in recognition and link selection and the most useful SERP document links may be those that best match the user's domain knowledge. User study participants rated their knowledge of genomics MeSH terms before conducting 2004 TREC Genomics Track tasks. Each participant's document knowledge was represented by their knowledge of the indexing MeSH terms. Results show high, intermediate, and low domain knowledge groups had similar document selection SERP rank distributions. SERP link selection distribution varied when participant knowledge of the available documents was analyzed. High domain knowledge participants usually selected a document with the highest personal knowledge rating. Low domain knowledge participants were reasonably successful at selecting available documents of which they had the most knowledge, while intermediate knowledge participants often failed to do so. This evidence for knowledge effects on SERP link selection may contribute to understanding the potential for personalization of search results ranking based on user domain knowledge.

In the industrial Internet of things (IIoT), there is always a strong demand for real-time information transfer. Especially when deploying wireless/wired hybrid networks in smart factories, the requirement for low delay interaction is more prominent. Although tree routing protocols have been successfully executed in simple networks, more challenges in transmission speed can be observed in the manufacturing broadband communication system. Motivated by the progresses in deep learning, a smart collaborative routing protocol with low delay and high reliability is proposed to accommodate mixed link scenarios. First, we establish a one-hop delay model to investigate the potential affects of Media Access Control (MAC) layer parameters, which supports the subsequent design. Second, forwarding, maintenance, and efficiency strategies are created to construct the basic functionalities for our routing protocol. Relevant procedures and key approaches are highlighted as well. Third, two sub-protocols are generated and the corresponding implementation steps are described. The experimental results demonstrate that the end-to-end delay can be effectively cut down through comprehensive improvements. Even more sensor nodes and larger network scale are involved, our proposed protocol can still illustrate the advantages comparing with existing solutions within IIoT. INDEX TERMS Industrial IoT, deep learning, routing protocol, tree topology, delay.