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Information Retrieval Technology

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Cover of 'Information Retrieval Technology'

Table of Contents

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    Book Overview
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    Chapter 1 Improving Retrieval Effectiveness for Temporal-Constrained Top-K Query Processing
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    Chapter 2 A Boosted Supervised Semantic Indexing for Reranking
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    Chapter 3 Improving Shard Selection for Selective Search
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    Chapter 4 Learning2extract for Medical Domain Retrieval
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    Chapter 5 FORK: Feedback-Aware ObjectRank-Based Keyword Search over Linked Data
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    Chapter 6 A Ranking Based Approach for Robust Object Discovery from Images of Mixed Classes
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    Chapter 7 Fast Exact Algorithm to Solve Continuous Similarity Search for Evolving Queries
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    Chapter 8 A Collaborative Neural Model for Rating Prediction by Leveraging User Reviews and Product Images
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    Chapter 9 Wide & Deep Learning in Job Recommendation: An Empirical Study
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    Chapter 10 A Neural Network Model for Social-Aware Recommendation
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    Chapter 11 Entity Ranking by Learning and Inferring Pairwise Preferences from User Reviews
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    Chapter 12 Probabilistic Local Matrix Factorization Based on User Reviews
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    Chapter 13 Finding and Quantifying Temporal-Aware Contradiction in Reviews
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    Chapter 14 Automatic Query Generation from Legal Texts for Case Law Retrieval
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    Chapter 15 An Investigation into the Use of Document Scores for Optimisation over Rank-Biased Precision
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    Chapter 16 Towards Privacy-Preserving Evaluation for Information Retrieval Models Over Industry Data Sets
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    Chapter 17 Evaluating Opinion Summarization in Ranking
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Title
Information Retrieval Technology
Published by
Springer, Cham, January 2017
DOI 10.1007/978-3-319-70145-5
ISBNs
978-3-31-970144-8, 978-3-31-970145-5
Editors

Won-Kyung Sung, Hanmin Jung, Shuo Xu, Krisana Chinnasarn, Kazutoshi Sumiya, Jeonghoon Lee, Zhicheng Dou, Grace Hui Yang, Young-Guk Ha, Seungbock Lee

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 1 100%
Readers by discipline Count As %
Computer Science 1 100%