PARK: Personalized academic retrieval with knowledge-graphs

TL;DR

This paper introduces PARK, a personalized academic search method that combines neural language models with knowledge graph embeddings to better capture user interests and improve retrieval effectiveness.

Contribution

It proposes a novel two-step approach integrating language models and knowledge graphs for personalized academic search, outperforming existing models in multiple domains.

Findings

Outperforms traditional models in 3 out of 4 domains

Achieves up to 10% improvement in MAP@100

Effectively captures explicit and hidden relationships in citation graphs

Abstract

Academic Search is a search task aimed to manage and retrieve scientific documents like journal articles and conference papers. Personalization in this context meets individual researchers' needs by leveraging, through user profiles, the user related information (e.g. documents authored by a researcher), to improve search effectiveness and to reduce the information overload. While citation graphs are a valuable means to support the outcome of recommender systems, their use in personalized academic search (with, e.g. nodes as papers and edges as citations) is still under-explored. Existing personalized models for academic search often struggle to fully capture users' academic interests. To address this, we propose a two-step approach: first, training a neural language model for retrieval, then converting the academic graph into a knowledge graph and embedding it into a shared semantic space with the language model using translational embedding techniques. This allows user models to capture both explicit relationships and hidden structures in citation graphs and paper content. We evaluate our approach in four academic search domains, outperforming traditional graph-based and personalized models in three out of four, with up to a 10\% improvement in MAP@100 over the second-best model. This highlights the potential of knowledge graph-based user models to enhance retrieval effectiveness.