Author-topic profiles for academic search

TL;DR

This paper presents a two-stage personalized academic search method that re-ranks initial results using author-topic profiles stored as graphs, improving relevance with interpretability and flexibility.

Contribution

The novel approach uses graph-based author-topic profiles for re-ranking, enhancing personalization and interpretability in academic search results.

Findings

Re-ranking improves search relevance significantly.

Graph-based profiles offer flexible and interpretable knowledge representation.

Method outperforms previous best approaches in literature.

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.