Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existin...
ISBN:
(纸本)9781713871088
Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a Diversity Control Regularization Scheme (DCRS) to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML.
Publishing articles in high-impact English journals is difficult for scholars around the world, especially for non-native English-speaking scholars (NNESs), most of whom struggle with proficiency in English. In order ...
Recent developments in unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) have provided users with flexible and resilient computing services. However, meeting the computing-intensive and latency-sensitive...
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Since the concern of privacy leakage extremely discourages user participation in sharing data, federated learning has gradually become a promising technique for both academia and industry for achieving collaborative l...
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Dear Editor,As an illuminating cancer hallmark,polymorphic microbiomes profoundly impact cancer phenotypes by promoting or repressing cancer initiation and progression.1 Diversity and composition in the gut microbiome...
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Dear Editor,As an illuminating cancer hallmark,polymorphic microbiomes profoundly impact cancer phenotypes by promoting or repressing cancer initiation and progression.1 Diversity and composition in the gut microbiome are significantly associated with the response rate of anti-PD1 immunotherapy in melanoma.2 In addition to the gut microbiome,a large number of microbiomes colonizing in human tumors have been shown to play significant roles in cancer development.3 However,a comprehensive understanding of intratumor microbiomes in cancer immunotherapy is lacking,largely due to the challenge of investigating intratumor microbiomes in anti-cancer immunotherapy.
In this paper, we investigate an unmanned aerial vehicle (UAV)-assistant air-to-ground communication system, where multiple UAVs form a UAV-enabled virtual antenna array (UVAA) to communicate with remote base stations...
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More and more user comments like Tweets are available, which often contain user concerns. In order to meet the demands of users, a good summary generating from multiple documents should consider reader interests as re...
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By introducing preplasma truncation to cases with an initial preplasma scale length larger than 0.2λ, the efficiency of high-order harmonics generated from relativistic laser-solid interactions can be enhanced by mor...
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By introducing preplasma truncation to cases with an initial preplasma scale length larger than 0.2λ, the efficiency of high-order harmonics generated from relativistic laser-solid interactions can be enhanced by more than one order of magnitude and the angular spread can be confined into near-diffraction-limited divergence. Numerical simulations show that density truncation results in more compact oscillation of the surface electron sheet and the curvature of the reflection surface for the driving laser is greatly reduced. This leads to an overall improvement in the harmonic beam quality. More importantly, density truncation makes the harmonic generation weakly dependent on the preplasma scale length, which provides a way to relax the extremely high requirement on the temporal contrast of the driving laser pulse. A feasible scheme to realize the required preplasma truncation is also proposed and demonstrated by numerical simulations.
Encoding only the task-related information from the raw data, i.e., disentangled representation learning, can greatly contribute to the robustness and generalizability of models. Although significant advances have bee...
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