Balancing Precision and Recall with Selective Search

Mon-Shih Chuang, Anagha Kulkarni
2017 Symposium on Information Management and Big Data  
This work revisits the age-old problem of balancing search precision and recall using the promising new approach of Selective Search which partitions the document collection into topic-based shards and searches a select few shards for any query. In prior work Selective Search has demonstrated strong search precision, however, this improvement has come at the cost of search recall. In this work, we test the hypothesis that improving the effectiveness of shard selection can better balance search
more » ... recision and recall. Toward this goal we investigate two new shard selection approaches, and conduct a series of experiments that lead to three new findings:-1. Big-document based shard selection approaches can substantially outperform the small-document approaches when provided with richer query representation, 2. Applying Learning-To-Rank approach for shard ranking provides the most effective Selective Search setup, 3. If the relevant documents for a query are spread across less than 10% of the shards then Selective Search can successfully balance precision and recall.
dblp:conf/simbig/ChuangK17 fatcat:7gckweeja5e6ta5tyim5paouuq