Evaluating Ensemble Methods for News Recommender Systems
Alexander Gray, Noorhan Abbas·June 23, 2024
Summary
This paper investigates the potential of ensemble methods in News Recommender Systems (NRS) to enhance performance. It evaluates diverse algorithms, such as BERT content-based and LSTUR collaborative filtering, on the Microsoft News Dataset (MIND), finding that combining diverse base learners can lead to up to a 5% improvement. However, combining non-distinct methods does not result in improvement. The study highlights the significance of selecting diverse algorithms for optimal ensemble results and suggests the need for further research in this area, while also acknowledging the challenges of cold-start problem and the evolving nature of data-driven methods like the poly attention mechanism in Miner. Performance metrics like AUC and MRR are used to assess the models, with some studies indicating that combining the best content-based and collaborative filtering methods can lead to substantial improvements. Ethical considerations, such as avoiding echo chambers and biases, are also mentioned as important for future research.
Introduction
Background
Evolution of News Recommender Systems (NRS)
Importance of personalization in news consumption
Objective
To assess the potential of ensemble methods in NRS
To compare diverse algorithms and their impact on performance
To address challenges like cold-start and data evolution
Methodology
Data Collection
Microsoft News Dataset (MIND) - Source and description
Dataset characteristics and preprocessing
Algorithm Selection and Evaluation
BERT Content-Based Approach
BERT model for news content understanding
Performance metrics (AUC, MRR)
LSTUR Collaborative Filtering
Latent Semantic Topic User Representation (LSTUR)
Collaborative filtering techniques
Performance evaluation
Ensemble Strategies
Combining BERT and LSTUR
Diverse vs. non-distinct base learners
Impact on performance improvement
Performance Analysis
AUC and MRR results
Substantial improvements with combined methods
Challenges and Future Research
Cold-start problem and its implications
Poly attention mechanism in Miner and its evolving nature
Ethical considerations (echo chambers, biases)
Conclusion
Significance of diverse algorithms in ensemble NRS
Recommendations for future research directions
Limitations and open questions in the field
Basic info
papers
information retrieval
artificial intelligence
Advanced features