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Found 12 papers, 4 papers with code
Understanding the Influence of Data Characteristics on the Performance of Point-of-Interest Recommendation Algorithms
The performance of recommendation algorithms is closely tied to key characteristics of the data sets they use, such as sparsity, popularity bias, and preference distributions.
Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis
The success of graph neural network-based models (GNNs) has significantly advanced recommender systems by effectively modeling users and items as a bipartite, undirected graph.
Top-N Recommendation Algorithms: A Quest for the State-of-the-Art
Moreover, we find that for none of the accuracy measurements any of the considered neural models led to the best performance.
Simulations for novel problems in recommendation: analyzing misinformation and data characteristics
In this position paper, we discuss recent applications of simulation approaches for recommender systems tasks.
Adherence and Constancy in LIME-RS Explanations for Recommendation
Explainable Recommendation has attracted a lot of attention due to a renewed interest in explainable artificial intelligence.
Explainable artificial intelligence
Explainable Recommendation
Reenvisioning Collaborative Filtering vs Matrix Factorization
We replicate experiments from three papers that compare Neural Collaborative Filtering (NCF) and Matrix Factorization (MF), to extend the analysis to other evaluation dimensions.
Point-of-Interest Recommender Systems based on Location-Based Social Networks: A Survey from an Experimental Perspective
Point-of-Interest recommendation is an increasing research and developing area within the widely adopted technologies known as Recommender Systems.
Analysing the Effect of Recommendation Algorithms on the Amplification of Misinformation
Recommendation algorithms have been pointed out as one of the major culprits of misinformation spreading in the digital sphere.
Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
Recommender Systems have shown to be an effective way to alleviate the over-choice problem and provide accurate and tailored recommendations.
Improving Accountability in Recommender Systems Research Through Reproducibility
In this work, we argue that, by facilitating reproducibility of recommender systems experimentation, we indirectly address the issues of accountability and transparency in recommender systems research from the perspectives of practitioners, designers, and engineers aiming to assess the capabilities of published research works.
Multi-Step Adversarial Perturbations on Recommender Systems Embeddings
However, while the single-step fast gradient sign method (FGSM) is the most explored perturbation strategy, multi-step (iterative) perturbation strategies, that demonstrated higher efficacy in the computer vision domain, have been highly under-researched in recommendation tasks.
A novel approach for venue recommendation using cross-domain techniques
We perform an experimental comparison of several recommendation techniques in a temporal split under two conditions: single-domain (only information from the target city is considered) and cross- domain (information from many other cities is incorporated into the recommendation algorithm).
