84 citations · 347 across the 30 of their papers we have counts for
10 papers · 1 filter
A Reproducibility Study of Product-side Fairness in Bundle Recommendation
Huy-Son Nguyen, Yuanna Liu, Masoud Mansoury +3
Recommender systems are known to exhibit fairness issues, particularly on the product side, where products and their associated suppliers receive unequal exposure in recommended re…
Towards Carbon Footprint-Aware Recommender Systems for Greener Item Recommendation
Raoul Kalisvaart, Masoud Mansoury, Alan Hanjalic +1
The commodity and widespread use of online shopping are having an unprecedented impact on climate, with emission figures from key actors that are easily comparable to those of a la…
Mitigating Mainstream Bias in Recommendation via Cost-sensitive Learning
Roger Zhe Li, Julián Urbano, Alan Hanjalic
Mainstream bias, where some users receive poor recommendations because their preferences are uncommon or simply because they are less active, is an important aspect to consider reg…
New Insights into Metric Optimization for Ranking-based Recommendation
Roger Zhe Li, Julián Urbano, Alan Hanjalic
Direct optimization of IR metrics has often been adopted as an approach to devise and develop ranking-based recommender systems. Most methods following this approach aim at optimiz…
Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users
Roger Zhe Li, Julián Urbano, Alan Hanjalic
In a collaborative-filtering recommendation scenario, biases in the data will likely propagate in the learned recommendations. In this paper we focus on the so-called mainstream bi…
Partially Synthetic Data for Recommender Systems: Prediction Performance and Preference Hiding
Manel Slokom, Martha Larson, Alan Hanjalic
This paper demonstrates the potential of statistical disclosure control for protecting the data used to train recommender systems. Specifically, we use a synthetic data generation…
Statistical Significance Testing in Information Retrieval: An Empirical Analysis of Type I, Type II and Type III Errors
Julián Urbano, Harlley Lima, Alan Hanjalic
Statistical significance testing is widely accepted as a means to assess how well a difference in effectiveness reflects an actual difference between systems, as opposed to random…
Factorization Machines for Data with Implicit Feedback
Babak Loni, Martha Larson, Alan Hanjalic
In this work, we propose FM-Pair, an adaptation of Factorization Machines with a pairwise loss function, making them effective for datasets with implicit feedback. The optimization…
GAPfm: Optimal Top-N Recommendations for Graded Relevance Domains
Yue Shi, Alexandros Karatzoglou, Linas Baltrunas +2
Recommender systems are frequently used in domains in which users express their preferences in the form of graded judgments, such as ratings. If accurate top-N recommendation lists…
Exploiting Social Tags for Cross-Domain Collaborative Filtering
Yue Shi, Martha Larson, Alan Hanjalic
One of the most challenging problems in recommender systems based on the collaborative filtering (CF) concept is data sparseness, i.e., limited user preference data is available fo…