Recommending Scientific Literature: Comparing Use-Cases and Algorithms

TL;DR

This paper compares different algorithms for recommending scientific literature, showing that hybrid methods outperform individual approaches across various user scenarios.

Contribution

It introduces four new datasets reflecting different relatedness scenarios and evaluates collaborative, content-based, and hybrid recommenders on these datasets.

Findings

Hybrid recommender achieves up to 70% precision.

Collaborative filtering slightly outperforms content-based filtering.

Different algorithms suit different user information needs.

Abstract

An important aspect of a researcher's activities is to find relevant and related publications. The task of a recommender system for scientific publications is to provide a list of papers that match these criteria. Based on the collection of publications managed by Mendeley, four data sets have been assembled that reflect different aspects of relatedness. Each of these relatedness scenarios reflect a user's search strategy. These scenarios are public groups, venues, author publications and user libraries. The first three of these data sets are being made publicly available for other researchers to compare algorithms against. Three recommender systems have been implemented: a collaborative filtering system; a content-based filtering system; and a hybrid of these two systems. Results from testing demonstrate that collaborative filtering slightly outperforms the content-based approach, but fails in some scenarios. The hybrid system, that combines the two recommendation methods, provides the best performance, achieving a precision of up to 70%. This suggests that both techniques contribute complementary information in the context of recommending scientific literature and different approaches suite for different information needs.