12 research outputs found
Local Clustering in Contextual Multi-Armed Bandits
We study identifying user clusters in contextual multi-armed bandits (MAB).
Contextual MAB is an effective tool for many real applications, such as content
recommendation and online advertisement. In practice, user dependency plays an
essential role in the user's actions, and thus the rewards. Clustering similar
users can improve the quality of reward estimation, which in turn leads to more
effective content recommendation and targeted advertising. Different from
traditional clustering settings, we cluster users based on the unknown bandit
parameters, which will be estimated incrementally. In particular, we define the
problem of cluster detection in contextual MAB, and propose a bandit algorithm,
LOCB, embedded with local clustering procedure. And, we provide theoretical
analysis about LOCB in terms of the correctness and efficiency of clustering
and its regret bound. Finally, we evaluate the proposed algorithm from various
aspects, which outperforms state-of-the-art baselines.Comment: 12 page
Graph Neural Bandits
Contextual bandits algorithms aim to choose the optimal arm with the highest
reward out of a set of candidates based on the contextual information. Various
bandit algorithms have been applied to real-world applications due to their
ability of tackling the exploitation-exploration dilemma. Motivated by online
recommendation scenarios, in this paper, we propose a framework named Graph
Neural Bandits (GNB) to leverage the collaborative nature among users empowered
by graph neural networks (GNNs). Instead of estimating rigid user clusters as
in existing works, we model the "fine-grained" collaborative effects through
estimated user graphs in terms of exploitation and exploration respectively.
Then, to refine the recommendation strategy, we utilize separate GNN-based
models on estimated user graphs for exploitation and adaptive exploration.
Theoretical analysis and experimental results on multiple real data sets in
comparison with state-of-the-art baselines are provided to demonstrate the
effectiveness of our proposed framework.Comment: Accepted to SIGKDD 202
Neural Bandit with Arm Group Graph
Contextual bandits aim to identify among a set of arms the optimal one with
the highest reward based on their contextual information. Motivated by the fact
that the arms usually exhibit group behaviors and the mutual impacts exist
among groups, we introduce a new model, Arm Group Graph (AGG), where the nodes
represent the groups of arms and the weighted edges formulate the correlations
among groups. To leverage the rich information in AGG, we propose a bandit
algorithm, AGG-UCB, where the neural networks are designed to estimate rewards,
and we propose to utilize graph neural networks (GNN) to learn the
representations of arm groups with correlations. To solve the
exploitation-exploration dilemma in bandits, we derive a new upper confidence
bound (UCB) built on neural networks (exploitation) for exploration.
Furthermore, we prove that AGG-UCB can achieve a near-optimal regret bound with
over-parameterized neural networks, and provide the convergence analysis of GNN
with fully-connected layers which may be of independent interest. In the end,
we conduct extensive experiments against state-of-the-art baselines on multiple
public data sets, showing the effectiveness of the proposed algorithm.Comment: Accepted to SIGKDD 202
Dynamic Knowledge Graph Alignment
Knowledge graph (KG for short) alignment aims at building a complete KG by linking the shared entities across complementary KGs. Existing approaches assume that KGs are static, despite the fact that almost every KG evolves over time. In this paper, we introduce the task of dynamic knowledge graph alignment, the main challenge of which is how to efficiently update entity embeddings for the evolving graph topology. Our key insight is to view the parameter matrix of GCN as a feature transformation operator and decouple the transformation process from the aggregation process. Based on that, we first propose a novel base algorithm (DINGAL-B) with topology-invariant mask gate and highway gate, which consistently outperforms 14 existing knowledge graph alignment methods in the static setting. More importantly, it naturally leads to two effective and efficient algorithms to align dynamic knowledge graph, including (1) DINGAL-O which leverages previous parameter matrices to update the embeddings of affected entities; and (2) DINGAL-U which resorts to newly obtained anchor links to fine-tune parameter matrices. Compared with their static counterpart (DINGAL-B), DINGAL-U and DINGAL-O are 10× and 100× faster respectively, with little alignment accuracy loss
DISCO: Comprehensive and Explainable Disinformation Detection
Disinformation refers to false information deliberately spread to influence
the general public, and the negative impact of disinformation on society can be
observed in numerous issues, such as political agendas and manipulating
financial markets. In this paper, we identify prevalent challenges and advances
related to automated disinformation detection from multiple aspects and propose
a comprehensive and explainable disinformation detection framework called
DISCO. It leverages the heterogeneity of disinformation and addresses the
opaqueness of prediction. Then we provide a demonstration of DISCO on a
real-world fake news detection task with satisfactory detection accuracy and
explanation. The demo video and source code of DISCO is now publicly available.
We expect that our demo could pave the way for addressing the limitations of
identification, comprehension, and explainability as a whole