Relevance feedback strategies for recall-oriented neural information
  retrieval
Timo Kats Peter van der Putten Jan Scholtes
In a number of information retrieval applications (e.g., patent search,
literature review, due diligence, etc.), preventing false negatives is more
important than preventing false positives. However, approaches designed to
reduce review effort (like "technology assisted review") can create false
negatives, since they are often based on active learning systems that exclude
documents automatically based on user feedback. Therefore, this research
proposes a more recall-oriented approach to reducing review effort. More
specifically, through iteratively re-ranking the relevance rankings based on
user feedback, which is also referred to as relevance feedback. In our proposed
method, the relevance rankings are produced by a BERT-based dense-vector search
and the relevance feedback is based on cumulatively summing the queried and
selected embeddings. Our results show that this method can reduce review effort
between 17.85% and 59.04%, compared to a baseline approach (of no feedback),
given a fixed recall target