ICTNET at Session Track TREC 2012

Abstract

In this paper, we describe our solutions to the Session Track at TREC 2012. The main contribution of our work is that we implement the learning to rank model to re-rank the documents retrieved by our search engine. We notice that Huurninket al. have used learning to rank algorithm to model session features at last year's Session Track. Due to lacking of training data, their model did not outperform substantially than others. Intuitively, we use last year's session data for tuning the weights of ranking features. Meanwhile, we define several useful features to model session search intent.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2012
Accession Number
ADA581239

Entities

People

  • Jun Chen
  • Junxiao Nan
  • Mingchuan Wei
  • Xiaoming Yu
  • Xueqi Cheng
  • Yue Liu
  • Zhenhong Chen

Organizations

  • Chinese Academy of Sciences

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Feature Selection
  • Governments
  • Information Operations
  • Instructions
  • Learning
  • Machine Learning
  • Optimization
  • Standards
  • Test And Evaluation

Fields of Study

  • Computer science

Readers

  • Information Retrieval
  • Neural Network Machine Learning.