84 citations · 347 across the 30 of their papers we have counts for
30 papers
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…
Estimating Nodal Spreading Influence Using Partial Temporal Network
Tianrui Mao, Shilun Zhang, Alan Hanjalic +1
Temporal networks, whose links are activated or deactivated over time, are used to represent complex systems such as social interactions or collaborations occurring at specific tim…
A data-centric approach for assessing progress of Graph Neural Networks
Tianqi Zhao, Ngan Thi Dong, Alan Hanjalic +1
Graph Neural Networks (GNNs) have achieved state-of-the-art results in node classification tasks. However, most improvements are in multi-class classification, with less focus on t…
AGALE: A Graph-Aware Continual Learning Evaluation Framework
Tianqi Zhao, Alan Hanjalic, Megha Khosla
In recent years, continual learning (CL) techniques have made significant progress in learning from streaming data while preserving knowledge across sequential tasks, particularly…
Predicting Nodal Influence via Local Iterative Metrics
Shilun Zhang, Alan Hanjalic, Huijuan Wang
Nodal spreading influence is the capability of a node to activate the rest of the network when it is the seed of spreading. Combining nodal properties (centrality metrics) derived…
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…
Multi-label Node Classification On Graph-Structured Data
Tianqi Zhao, Ngan Thi Dong, Alan Hanjalic +1
Graph Neural Networks (GNNs) have shown state-of-the-art improvements in node classification tasks on graphs. While these improvements have been largely demonstrated in a multi-cla…
Evaluating the Impact of Tiled User-Adaptive Real-Time Point Cloud Streaming on VR Remote Communication
Shishir Subramanyam, Irene Viola, Jack Jansen +3
Remote communication has rapidly become a part of everyday life in both professional and personal contexts. However, popular video conferencing applications present limitations in…
Topological-temporal properties of evolving networks
Alberto Ceria, Shlomo Havlin, Alan Hanjalic +1
Many real-world complex systems including human interactions can be represented by temporal (or evolving) networks, where links activate or deactivate over time. Characterizing tem…
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…
S2IGAN: Speech-to-Image Generation via Adversarial Learning
Xinsheng Wang, Tingting Qiao, Jihua Zhu +2
An estimated half of the world's languages do not have a written form, making it impossible for these languages to benefit from any existing text-based technologies. In this paper,…
Matching Images and Text with Multi-modal Tensor Fusion and Re-ranking
Tan Wang, Xing Xu, Yang Yang +3
A major challenge in matching images and text is that they have intrinsically different data distributions and feature representations. Most existing approaches are based either on…
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…
Are Nearby Neighbors Relatives?: Testing Deep Music Embeddings
Jaehun Kim, Julián Urbano, Cynthia C. S. Liem +1
Deep neural networks have frequently been used to directly learn representations useful for a given task from raw input data. In terms of overall performance metrics, machine learn…
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…
One Deep Music Representation to Rule Them All? : A comparative analysis of different representation learning strategies
Jaehun Kim, Julián Urbano, Cynthia C. S. Liem +1
Inspired by the success of deploying deep learning in the fields of Computer Vision and Natural Language Processing, this learning paradigm has also found its way into the field of…
Binary Generative Adversarial Networks for Image Retrieval
Jingkuan Song
The most striking successes in image retrieval using deep hashing have mostly involved discriminative models, which require labels. In this paper, we use binary generative adversar…
From Deterministic to Generative: Multi-Modal Stochastic RNNs for Video Captioning
Jingkuan Song, Yuyu Guo, Lianli Gao +3
Video captioning in essential is a complex natural process, which is affected by various uncertainties stemming from video content, subjective judgment, etc. In this paper we build…
Vision-based Detection of Acoustic Timed Events: a Case Study on Clarinet Note Onsets
A. Bazzica, J. C. van Gemert, C. C. S. Liem +1
Acoustic events often have a visual counterpart. Knowledge of visual information can aid the understanding of complex auditory scenes, even when only a stereo mixdown is available…
Modeling of Information Diffusion on Social Networks with Applications to WeChat
Liang Liu, Bo Qu, Bin Chen +2
Traces of user activities recorded in online social networks such as the creation, viewing and forwarding/sharing of information over time open new possibilities to quantitatively…
Where to be wary: The impact of widespread photo-taking and image enhancement practices on users' geo-privacy
Jaeyoung Choi, Martha Larson, Xinchao Li +2
Today's geo-location estimation approaches are able to infer the location of a target image using its visual content alone. These approaches exploit visual matching techniques, app…