Author-topic profiles for academic search
Abstract
We implemented and evaluated a two-stage retrieval method for personalized academic search in which the initial search results are re-ranked using an author-topic profile. In academic search tasks, the user's own data can help optimizing the ranking of search results to match the searcher's specific individual needs. The author-topic profile consists of topic-specific terms, stored in a graph. We re-rank the top-1000 retrieved documents using ten features that represent the similarity between the document and the author-topic graph. We found that the re-ranking gives a small but significant improvement over the reproduced best method from the literature. Storing the profile as a graph has a number of advantages: it is flexible with respect to node and relation types; it is a visualization of knowledge that is interpretable by the user, and it offers the possibility to view relational characteristics of individual nodes.
Cite
@article{arxiv.1804.11131,
title = {Author-topic profiles for academic search},
author = {Suzan Verberne and Arjen P. de Vries and Wessel Kraaij},
journal= {arXiv preprint arXiv:1804.11131},
year = {2018}
}
Comments
13 pages, 1 figure