REVIEW 2 cited by
Recent Developments in Recommender Systems: A Survey
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
In this technical survey, we comprehensively summarize the latest advancements in the field of recommender systems. The objective of this study is to provide an overview of the current state-of-the-art in the field and highlight the latest trends in the development of recommender systems. The study starts with a comprehensive summary of the main taxonomy of recommender systems, including personalized and group recommender systems, and then delves into the category of knowledge-based recommender systems. In addition, the survey analyzes the robustness, data bias, and fairness issues in recommender systems, summarizing the evaluation metrics used to assess the performance of these systems. Finally, the study provides insights into the latest trends in the development of recommender systems and highlights the new directions for future research in the field.
Forward citations
Cited by 2 Pith papers
-
Equity by Design? On the Trade-Offs in Fairness-Driven Recommendation in Heterogeneous Two-Sided Markets
The 'free fairness' result for producer constraints vanishes for multi-item recommendations; a CVaR group-fairness objective and business constraints can be added with moderate trade-offs.
-
UniMLR: Modeling Implicit Class Significance for Multi-Label Ranking
UniMLR uses positive-positive label pairs and Gaussian significance scores to unify multi-label classification and ranking, evaluated on new Ranked MNIST datasets.
Discussion (0). Sign in to comment.