Appeared in: First Workshop on Proactive and Agent-Supported Information Retrieval (PASIR@CIKM 2022)
Abstract:
Re-ranking systems are typically limited by the recall of the initial retrieval function. A recent work proposed adaptive re-ranking, which modifies the re-ranking loop to progressively prioritise documents likely to receive high scores based on the highest scoring ones thus far. The original work framed this process as an incarnation of the well-established clustering hypothesis. In this work, we argue that the approach can also be framed as an information-seeking agent. From this perspective, we explore several variations of the graph-based adaptive re-ranking algorithm and find that there is substantial room for improvement by modifying the agent. However, the agents that we explore are more sensitive to the new parameters they introduce than the simple-yet-effective approach proposed in the original adaptive re-ranking work.
BibTeX @inproceedings{macavaney:pasir2022-gagent, author = {MacAvaney, Sean and Tonellotto, Nicola and Macdonald, Craig}, title = {Adaptive Re-Ranking as an Information-Seeking Agent}, booktitle = {First Workshop on Proactive and Agent-Supported Information Retrieval}, year = {2022} }