PARK: Personalized academic retrieval with knowledge-graphs
Pranav Kasela, Gabriella Pasi, Raffaele Perego

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…
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