Improving Technical "How-to" Query Accuracy with Automated Search
  Results Verification and Reranking
Lei Ding Jeshwanth Bheemanpally Yi Zhang
Many people use search engines to find online guidance to solve computer or
mobile device problems. Users frequently encounter challenges in identifying
effective solutions from search results, often wasting time trying ineffective
solutions that seem relevant yet fail to solve the real problems. This paper
introduces a novel approach to improving the accuracy and relevance of online
technical support search results through automated search results verification
and reranking. Taking "How-to" queries specific to on-device execution as a
starting point, we first developed a solution that allows an AI agent to
interpret and execute step-by-step instructions in the search results in a
controlled Android environment. We further integrated the agent's findings into
a reranking mechanism that orders search results based on the success
indicators of the tested solutions.
  The paper details the architecture of our solution and a comprehensive
evaluation of the system through a series of tests across various application
domains. The results demonstrate a significant improvement in the quality and
reliability of the top-ranked results. Our findings suggest a paradigm shift in
how search engine ranking for online technical support help can be optimized,
offering a scalable and automated solution to the pervasive challenge of
finding effective and reliable online help.