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Found 15 papers, 9 papers with code
Mitigating Mainstream Bias in Recommendation via Cost-sensitive Learning
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 regarding fairness in recommender systems.
Multi-label Node Classification On Graph-Structured Data
As our second contribution, we define homophily and Cross-Class Neighborhood Similarity for the multi-label scenario and provide a thorough analyses of the collected $9$ multi-label datasets.
New Insights into Metric Optimization for Ranking-based Recommendation
Most methods following this approach aim at optimizing the same metric being used for evaluation, under the assumption that this will lead to the best performance.
Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users
In this paper we focus on the so-called mainstream bias: the tendency of a recommender system to provide better recommendations to users who have a mainstream taste, as opposed to non-mainstream users.
Partially Synthetic Data for Recommender Systems: Prediction Performance and Preference Hiding
This paper demonstrates the potential of statistical disclosure control for protecting the data used to train recommender systems.
S2IGAN: Speech-to-Image Generation via Adversarial Learning
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.
Matching Images and Text with Multi-modal Tensor Fusion and Re-ranking
We propose a novel framework that achieves remarkable matching performance with acceptable model complexity.
Statistical Significance Testing in Information Retrieval: An Empirical Analysis of Type I, Type II and Type III Errors
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 noise because of the selection of topics.
Are Nearby Neighbors Relatives?: Testing Deep Music Embeddings
The underlying assumption is that in case a deep representation is to be trusted, distance consistency between known related points should be maintained both in the input audio space and corresponding latent deep space.
One Deep Music Representation to Rule Them All? : A comparative analysis of different representation learning strategies
In this paper, we present the results of our investigation of what are the most important factors to generate deep representations for the data and learning tasks in the music domain.
From Deterministic to Generative: Multi-Modal Stochastic RNNs for Video Captioning
In this paper, we propose a generative approach, referred to as multi-modal stochastic RNNs networks (MS-RNN), which models the uncertainty observed in the data using latent stochastic variables.
Geo-distinctive Visual Element Matching for Location Estimation of Images
These representations are based on visual element clouds, which robustly capture the connection between the query and visual evidence from candidate locations.
Learning Subclass Representations for Visually-varied Image Classification
The novelty of the approach is that subclass representations make use of not only the content of the photos themselves, but also information on the co-occurrence of their tags, which determines membership in both subclasses and top-level classes.
Pairwise Geometric Matching for Large-Scale Object Retrieval
Spatial verification is a key step in boosting the performance of object-based image retrieval.
Exploiting Social Tags for Cross-Domain Collaborative Filtering
A key question to be answered in the context of CDCF is what common characteristics can be deployed to link different domains for effective knowledge transfer.
