134 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
DPPIN: A Biological Dataset of Dynamic Protein-Protein Interaction Networks
Nowadays, many network representation learning algorithms and downstream
network mining tasks have already paid attention to dynamic networks or
temporal networks, which are more suitable for real-world complex scenarios by
modeling evolving patterns and temporal dependencies between node interactions.
Moreover, representing and mining temporal networks have a wide range of
applications, such as fraud detection, social network analysis, and drug
discovery. To contribute to the network representation learning and network
mining research community, in this paper, we generate a new biological dataset
of dynamic protein-protein interaction networks (i.e., DPPIN), which consists
of twelve dynamic protein-level interaction networks of yeast cells at
different scales. We first introduce the generation process of DPPIN. To
demonstrate the value of our published dataset DPPIN, we then list the
potential applications that would be benefited. Furthermore, we design dynamic
local clustering, dynamic spectral clustering, dynamic subgraph matching,
dynamic node classification, and dynamic graph classification experiments,
where DPPIN indicates future research opportunities for some tasks by
presenting challenges on state-of-the-art baseline algorithms. Finally, we
identify future directions for improving this dataset utility and welcome
inputs from the community. All resources of this work are deployed and publicly
available at https://github.com/DongqiFu/DPPIN
Robust Basket Recommendation via Noise-tolerated Graph Contrastive Learning
The growth of e-commerce has seen a surge in popularity of platforms like
Amazon, eBay, and Taobao. This has given rise to a unique shopping behavior
involving baskets - sets of items purchased together. As a less studied
interaction mode in the community, the question of how should shopping basket
complement personalized recommendation systems remains under-explored. While
previous attempts focused on jointly modeling user purchases and baskets, the
distinct semantic nature of these elements can introduce noise when directly
integrated. This noise negatively impacts the model's performance, further
exacerbated by significant noise (e.g., a user is misled to click an item or
recognizes it as uninteresting after consuming it) within both user and basket
behaviors. In order to cope with the above difficulties, we propose a novel
Basket recommendation framework via Noise-tolerated Contrastive Learning, named
BNCL, to handle the noise existing in the cross-behavior integration and
within-behavior modeling. First, we represent the basket-item interactions as
the hypergraph to model the complex basket behavior, where all items appearing
in the same basket are treated as a single hyperedge. Second, cross-behavior
contrastive learning is designed to suppress the noise during the fusion of
diverse behaviors. Next, to further inhibit the within-behavior noise of the
user and basket interactions, we propose to exploit invariant properties of the
recommenders w.r.t augmentations through within-behavior contrastive learning.
A novel consistency-aware augmentation approach is further designed to better
identify noisy interactions with the consideration of the above two types of
interactions. Our framework BNCL offers a generic training paradigm that is
applicable to different backbones. Extensive experiments on three shopping
transaction datasets verify the effectiveness of our proposed method.Comment: CIKM 202
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
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