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Vector Space Proximity Based Document Retrieval For Document Embeddings Built By Transformers

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Vector Space Proximity Based Document Retrieval For Document Embeddings Built By Transformers

Khloponin, Pavel (2022) Vector Space Proximity Based Document Retrieval For Document Embeddings Built By Transformers. Masters thesis, Concordia University.

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Abstract

Internet publications are staying atop of local and international events, generating hundreds,
sometimes thousands of news articles per day, making it difficult for readers to navigate this stream
of information without assistance. Competition for the reader’s attention has never been greater.
One strategy to keep readers’ attention on a specific article and help them better understand its
content is news recommendation, which automatically provides readers with references to relevant
complementary articles. However, to be effective, news recommendation needs to select from a
large collection of candidate articles only a handful of articles that are relevant yet provide diverse
information.
In this thesis, we propose and experiment with three methods for news recommendation and
evaluate them in the context of the NIST News Track. Our first approach is based on the classic
BM25 information retrieval approach and assumes that relevant articles will share common key-
words with the current article. Our second approach is based on novel document embedding repre-
sentations and uses various proximity measures to retrieve the closest documents. For this approach,
we experimented with a substantial number of models, proximity measures, and hyperparameters,
yielding a total of 47,332 distinct models. Finally, our third approach combines the BM25 and the
embedding models to increase the diversity of the results.
The results on the 2020 TREC News Track show that the performance of the BM25 model
(nDCG@5 of 0.5924) greatly exceeds the TREC median performance (nDCG@5 of 0.5250) and
achieves the highest score at the shared task. The performance of the embedding model alone
(nDCG@5 of 0.4541) is lower than the TREC median and BM25. The performance of the combined
model (nDCG@5 of 0.5873) is rather close to that of the BM25 model; however, an analysis of the
results shows that the recommended articles are different from those proposed by BM25, hence may
constitute a promising approach to reach diversity without much loss in relevance.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:Khloponin, Pavel
Institution:Concordia University
Degree Name:M. Comp. Sc.
Program:Computer Science
Date:June 2022
Thesis Supervisor(s):Kosseim, Leila
Keywords:Background linking Document embedding Proximity measures
ID Code:990826
Deposited By: PAVEL KHLOPONIN
Deposited On:27 Oct 2022 14:38
Last Modified:27 Oct 2022 14:38

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