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Found 26 papers, 7 papers with code
Term-Recency for TF-IDF, BM25 and USE Term Weighting
Term weighting is responsible for computing the relevance scores and consequently differentiating between the terms in a document.
Virtual Citation Proximity (VCP): Empowering Document Recommender Systems by Learning a Hypothetical In-Text Citation-Proximity Metric for Uncited Documents
The prediction can be used in the same way as real citation proximity to calculate document relatedness, even for uncited documents.
The Potential of AutoML for Recommender Systems
We found that AutoML and AutoRecSys libraries performed best.
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML
Automated machine learning (AutoML) systems commonly ensemble models post hoc to improve predictive performance, typically via greedy ensemble selection (GES).
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure
Consequently, we compared the performance of covariance matrix adaptation evolution strategy (CMA-ES), state-of-the-art gradient-free numerical optimization, to GES on the 71 classification datasets from the AutoML benchmark for AutoGluon.
Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenML
Moreover, we present an example of using Assembled-OpenML to compare a set of ensemble techniques.
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.
Finite Group Equivariant Neural Networks for Games
FGNNs are shown to improve the performance of networks playing checkers (draughts), and can be easily adapted to other games and learning problems.
Towards an Interoperable Data Protocol Aimed at Linking the Fashion Industry with AI Companies
As a result, AI companies are relying on manually annotated fashion data to build different applications.
Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization
Auto-Surprise is an extension of the Surprise recommender system library and eases the algorithm selection and configuration process.
Siamese Meta-Learning and Algorithm Selection with 'Algorithm-Performance Personas' [Proposal]
Our work includes a novel performance metric and method for selecting training samples.
Synthetic vs. Real Reference Strings for Citation Parsing, and the Importance of Re-training and Out-Of-Sample Data for Meaningful Evaluations: Experiments with GROBID, GIANT and Cora
We find that both synthetic and organic reference strings are equally suited for training Grobid (F1 = 0. 74).
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%).
Multi-stream Data Analytics for Enhanced Performance Prediction in Fantasy Football
Fantasy Premier League (FPL) performance predictors tend to base their algorithms purely on historical statistical data.
Predicting the Outcome of Judicial Decisions made by the European Court of Human Rights
The heuristic achieved an overall test accuracy of 86. 68% which is 29. 7% higher than the models.
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.
Memory-Augmented Neural Networks for Machine Translation
Memory-augmented neural networks (MANNs) have been shown to outperform other recurrent neural network architectures on a series of artificial sequence learning tasks, yet they have had limited application to real-world tasks.
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.
ParsRec: A Novel Meta-Learning Approach to Recommending Bibliographic Reference Parsers
We propose ParsRec, a meta-learning based recommender-system that recommends the potentially most effective parser for a given reference string.
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.
An Empirical Comparison of Syllabuses for Curriculum Learning
Our experimental results provide an empirical basis for the choice of syllabus on a new problem that could benefit from curriculum learning.
Implementing Neural Turing Machines
Our implementation learns to solve three sequential learning tasks from the original NTM paper.
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
