BJUT at TREC 2015 Contextual Suggestion Track

Abstract

In this paper we described our efforts for TREC contextual suggestion task. Our goal of this year is to evaluate the effectiveness of: (1) predict user preferences of each scenic spot based on non-negtive matrix factorization, (2) automatic summarization method that leverages the information from multiple resources to generate the description for each candidate scenic spots; and (3) hybrid recommendation method that combing a variety of factors to construct a system of hybrid recommendation system. Finally, we conduct extensive experiments to evaluate the proposed framework on TREC 2015 Contextual Suggestion dataset, and, as would be expected, the results demonstrate its generality and superior performance.

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Document Details

Document Type
Technical Report
Publication Date
Nov 20, 2015
Accession Number
AD1004703

Entities

People

  • Hanchen Li
  • Weitong Chen
  • Zhen Yang

Organizations

  • Beijing University of Technology

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Automata Theory
  • Automated Text Summarization
  • Classification
  • Computer Science
  • Data Fusion
  • Data Mining
  • Demographic Cohorts
  • Earth Sciences
  • Expert Systems
  • Information Science
  • Machine Learning
  • Network Science
  • Remote Sensing
  • Statistics
  • Universities

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Information Retrieval