  As more and more search traffic comes from mobile phones, intelligent
assistants, and smart-home devices, new challenges (e.g., limited presentation
space) and opportunities come up in information retrieval. Previously, an
effective technique, relevance feedback (RF), has rarely been used in real
search scenarios due to the overhead of collecting users' relevance judgments.
However, since users tend to interact more with the search results shown on the
new interfaces, it becomes feasible to obtain users' assessments on a few
results during each interaction. This makes iterative relevance feedback (IRF)
techniques look promising today. IRF has not been studied systematically in the
new search scenarios and its effectiveness is mostly unknown. In this paper, we
re-visit IRF and extend it with RF models proposed in recent years. We conduct
extensive experiments to analyze and compare IRF with the standard top-k RF
framework on document and passage retrieval. Experimental results show that IRF
is at least as effective as the standard top-k RF framework for documents and
much more effective for passages. This indicates that IRF for passage retrieval
has huge potential.
