Search Results for author: J. Shane Culpepper

Found 5 papers, 4 papers with code

Spatial Object Recommendation with Hints: When Spatial Granularity Matters

no code implementations8 Jan 2021 Hui Luo, Jingbo Zhou, Zhifeng Bao, Shuangli Li, J. Shane Culpepper, Haochao Ying, Hao liu, Hui Xiong

We design a novel multi-task learning model called MPR (short for Multi-level POI Recommendation), where each task aims to return the top-k POIs at a certain spatial granularity level.

Multi-Task Learning Representation Learning


1 code implementation9 Nov 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.

Temporal Network Representation Learning via Historical Neighborhoods Aggregation

1 code implementation30 Mar 2020 Shixun Huang, Zhifeng Bao, Guoliang Li, Yanghao Zhou, J. Shane Culpepper

More specifically, we first propose a temporal random walk that can identify relevant nodes in historical neighborhoods which have impact on edge formations.

Link Prediction Network Embedding +1

Joint Optimization of Cascade Ranking Models

1 code implementation WSDM 2019 Luke Gallagher, Ruey-Chen Chen, Roi Blanco, J. Shane Culpepper

A cascaded ranking architecture turns ranking into a pipeline of multiple stages, and has been shown to be a powerful approach to balancing efficiency and effectiveness trade-offs in large-scale search systems.

Document Ranking Information Retrieval +1

Boosting Search Performance Using Query Variations

1 code implementation15 Nov 2018 Rodger Benham, Joel Mackenzie, Alistair Moffat, J. Shane Culpepper

Rank fusion is a powerful technique that allows multiple sources of information to be combined into a single result set.


Cannot find the paper you are looking for? You can Submit a new open access paper.