Zobrazeno 1 - 10
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pro vyhledávání: '"Yang, Yongjian"'
With the rapid development of various sensing devices, spatiotemporal data is becoming increasingly important nowadays. However, due to sensing costs and privacy concerns, the collected data is often incomplete and coarse-grained, limiting its applic
Externí odkaz:
http://arxiv.org/abs/2410.05323
Autor:
Yang, Yongjian
This dissertation explores the application of machine learning in molecular biology, focusing on gene expression regulation and cellular behavior at the single-cell level. Using modern neural networks, the research addresses key challenges in cell-ce
Externí odkaz:
http://arxiv.org/abs/2409.19482
Recommendation models trained on the user feedback collected from deployed recommendation systems are commonly biased. User feedback is considerably affected by the exposure mechanism, as users only provide feedback on the items exposed to them and p
Externí odkaz:
http://arxiv.org/abs/2311.05864
Inferring user preferences from the historical feedback of users is a valuable problem in recommender systems. Conventional approaches often rely on the assumption that user preferences in the feedback data are equivalent to the real user preferences
Externí odkaz:
http://arxiv.org/abs/2311.03382
Recommender models aim to capture user preferences from historical feedback and then predict user-specific feedback on candidate items. However, the presence of various unmeasured confounders causes deviations between the user preferences in the hist
Externí odkaz:
http://arxiv.org/abs/2311.03381
Data analysis usually suffers from the Missing Not At Random (MNAR) problem, where the cause of the value missing is not fully observed. Compared to the naive Missing Completely At Random (MCAR) problem, it is more in line with the realistic scenario
Externí odkaz:
http://arxiv.org/abs/2308.08158
In this paper, we present a MLP-like architecture for sequential recommendation, namely TriMLP, with a novel Triangular Mixer for cross-token communications. In designing Triangular Mixer, we simplify the cross-token operation in MLP as the basic mat
Externí odkaz:
http://arxiv.org/abs/2305.14675
In e-commerce, online retailers are usually suffering from professional malicious users (PMUs), who utilize negative reviews and low ratings to their consumed products on purpose to threaten the retailers for illegal profits. Specifically, there are
Externí odkaz:
http://arxiv.org/abs/2205.09673
Most NN-RSs focus on accuracy by building representations from the direct user-item interactions (e.g., user-item rating matrix), while ignoring the underlying relatedness between users and items (e.g., users who rate the same ratings for the same it
Externí odkaz:
http://arxiv.org/abs/2205.09670
Classical accuracy-oriented Recommender Systems (RSs) typically face the cold-start problem and the filter-bubble problem when users suffer the familiar, repeated, and even predictable recommendations, making them boring and unsatisfied. To address t
Externí odkaz:
http://arxiv.org/abs/2204.12651