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20132021
most citedSpatial Object Recommendation with Hints: When Spatial Granularity Matters

6 citations · 6 across the 5 of their papers we have counts for

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cs.IR20216 cited

Spatial Object Recommendation with Hints: When Spatial Granularity Matters

Hui Luo, Jingbo Zhou, Zhifeng Bao +5

Existing spatial object recommendation algorithms generally treat objects identically when ranking them. However, spatial objects often cover different levels of spatial granularit…

cs.IR2020

RMITB at TREC COVID 2020

Rodger Benham, Alistair Moffat, J. Shane Culpepper

Search engine users rarely express an information need using the same query, and small differences in queries can lead to very different result sets. These user query variations ha…

cs.IR2018

The Potential of Learned Index Structures for Index Compression

Harrie Oosterhuis, J. Shane Culpepper, Maarten de Rijke

Inverted indexes are vital in providing fast key-word-based search. For every term in the document collection, a list of identifiers of documents in which the term appears is store…

cs.IR2018

Boosting Search Performance Using Query Variations

Rodger Benham, Joel Mackenzie, Alistair Moffat +1

Rank fusion is a powerful technique that allows multiple sources of information to be combined into a single result set. However, to date fusion has not been regarded as being cost…

cs.IR2015

Assessing Efficiency-Effectiveness Tradeoffs in Multi-Stage Retrieval Systems Without Using Relevance Judgments

Charles L. A. Clarke, J. Shane Culpepper, Alistair Moffat

Large-scale retrieval systems are often implemented as a cascading sequence of phases -- a first filtering step, in which a large set of candidate documents are extracted using a s…