Context-Aware Recommendations with Random Partition Factorization Machines
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DOI: 10.1007/s41019-017-0035-3
Abstract: Abstract Context plays an important role in helping users to make decisions. There are hierarchical structure between contexts and aggregation characteristics within the context in real scenarios. Exist works mainly focus on exploring the explicit… read more here.
Keywords: context; random partition; context aware; partition factorization ... See more keywords
Landslide susceptibility mapping by attentional factorization machines considering feature interactions
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DOI: 10.1080/19475705.2021.1950217
Abstract: Landslide susceptibility mapping (LSM) is a commonly used approach to reduce landslide risk. However, conventional LSM methods generally only consider the influence of each single conditioning fact... read more here.
Keywords: attentional factorization; mapping attentional; susceptibility mapping; landslide susceptibility ... See more keywords
Hierarchical Attentional Factorization Machines for Expert Recommendation in Community Question Answering
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DOI: 10.1109/access.2020.2974893
Abstract: The most challenging task of Community Question Answering (CQA) is to provide high-quality answers to users’ questions. Currently, a variety of expert recommendation methods have been proposed and greatly improved the effective matching between questions… read more here.
Keywords: community question; factorization machines; question; recommendation ... See more keywords
Graph-Convolved Factorization Machines for Personalized Recommendation
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DOI: 10.1109/tkde.2021.3100564
Abstract: Factorization machines (FMs) and their neural network variants (neural FMs) for modeling second-order feature interactions are effective in building modern recommendation systems. However, feature interactions are based upon pairs of features, whereas multi-features correlations commonly… read more here.
Keywords: recommendation; feature; graph convolved; graph ... See more keywords