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Found 10 papers, 0 papers with code
Per-Instance Algorithm Selection for Recommender Systems via Instance Clustering
To the best of our knowledge, this is the first effective meta-learning technique for per-instance algorithm selection in recommender systems.
Siamese Meta-Learning and Algorithm Selection with 'Algorithm-Performance Personas' [Proposal]
Our work includes a novel performance metric and method for selecting training samples.
Meta-Learned Per-Instance Algorithm Selection in Scholarly Recommender Systems
User engagement was significantly increased for recommendations generated using our meta-learning approach when compared to a random selection of algorithm (Click-through rate (CTR); 0. 51% vs. 0. 44%, Chi-Squared test; p < 0. 1), however our approach did not produce a higher CTR than the best algorithm alone (CTR; MoreLikeThis (Title): 0. 58%).
NaïveRole: Author-Contribution Extraction and Parsing from Biomedical Manuscripts
In this paper, we present 1) A statistical analysis of roles in author contributions sections, and 2) Na\"iveRole, a novel approach to extract structured authors' roles from author contribution sections.
Document Embeddings vs. Keyphrases vs. Terms: An Online Evaluation in Digital Library Recommender Systems
There is a ~400% difference in effectiveness between the best and worst algorithm in both scenarios separately.
The Architecture of Mr. DLib's Scientific Recommender-System API
In this paper, we introduce Mr. DLib's "Recommendations as-a-Service" (RaaS) API that allows operators of academic products to easily integrate a scientific recommender system into their products.
Online Evaluations for Everyone: Mr. DLib's Living Lab for Scholarly Recommendations
We present the architecture of Mr. DLib's living lab as well as usage statistics on the first sixteen months of operating it.
RARD II: The 94 Million Related-Article Recommendation Dataset
The main contribution of this paper is to introduce and describe a new recommender-systems dataset (RARD II).
A Study of Position Bias in Digital Library Recommender Systems
We conduct a study in a real-world recommender system that delivered ten million related-article recommendations to the users of the digital library Sowiport, and the reference manager JabRef.
A Method for Discovering and Extracting Author Contributions Information from Scientific Biomedical Publications
In this paper, we present an analysis of roles commonly appearing in the content of these sections, and propose an algorithm for automatic extraction of authors' roles from natural language text in scientific publications.
Digital Libraries
