16 research outputs found
Explicit Feature Interaction-aware Uplift Network for Online Marketing
As a key component in online marketing, uplift modeling aims to accurately
capture the degree to which different treatments motivate different users, such
as coupons or discounts, also known as the estimation of individual treatment
effect (ITE). In an actual business scenario, the options for treatment may be
numerous and complex, and there may be correlations between different
treatments. In addition, each marketing instance may also have rich user and
contextual features. However, existing methods still fall short in both fully
exploiting treatment information and mining features that are sensitive to a
particular treatment. In this paper, we propose an explicit feature
interaction-aware uplift network (EFIN) to address these two problems. Our EFIN
includes four customized modules: 1) a feature encoding module encodes not only
the user and contextual features, but also the treatment features; 2) a
self-interaction module aims to accurately model the user's natural response
with all but the treatment features; 3) a treatment-aware interaction module
accurately models the degree to which a particular treatment motivates a user
through interactions between the treatment features and other features, i.e.,
ITE; and 4) an intervention constraint module is used to balance the ITE
distribution of users between the control and treatment groups so that the
model would still achieve a accurate uplift ranking on data collected from a
non-random intervention marketing scenario. We conduct extensive experiments on
two public datasets and one product dataset to verify the effectiveness of our
EFIN. In addition, our EFIN has been deployed in a credit card bill payment
scenario of a large online financial platform with a significant improvement.Comment: Accepted by SIGKDD 2023 Applied Data Science Trac
Constructing Sample-to-Class Graph for Few-Shot Class-Incremental Learning
Few-shot class-incremental learning (FSCIL) aims to build machine learning
model that can continually learn new concepts from a few data samples, without
forgetting knowledge of old classes.
The challenges of FSCIL lies in the limited data of new classes, which not
only lead to significant overfitting issues but also exacerbates the notorious
catastrophic forgetting problems. As proved in early studies, building sample
relationships is beneficial for learning from few-shot samples. In this paper,
we promote the idea to the incremental scenario, and propose a Sample-to-Class
(S2C) graph learning method for FSCIL.
Specifically, we propose a Sample-level Graph Network (SGN) that focuses on
analyzing sample relationships within a single session. This network helps
aggregate similar samples, ultimately leading to the extraction of more refined
class-level features.
Then, we present a Class-level Graph Network (CGN) that establishes
connections across class-level features of both new and old classes. This
network plays a crucial role in linking the knowledge between different
sessions and helps improve overall learning in the FSCIL scenario. Moreover, we
design a multi-stage strategy for training S2C model, which mitigates the
training challenges posed by limited data in the incremental process.
The multi-stage training strategy is designed to build S2C graph from base to
few-shot stages, and improve the capacity via an extra pseudo-incremental
stage. Experiments on three popular benchmark datasets show that our method
clearly outperforms the baselines and sets new state-of-the-art results in
FSCIL
Result Diversification in Search and Recommendation: A Survey
Diversifying return results is an important research topic in retrieval
systems in order to satisfy both the various interests of customers and the
equal market exposure of providers. There has been growing attention on
diversity-aware research during recent years, accompanied by a proliferation of
literature on methods to promote diversity in search and recommendation.
However, diversity-aware studies in retrieval systems lack a systematic
organization and are rather fragmented. In this survey, we are the first to
propose a unified taxonomy for classifying the metrics and approaches of
diversification in both search and recommendation, which are two of the most
extensively researched fields of retrieval systems. We begin the survey with a
brief discussion of why diversity is important in retrieval systems, followed
by a summary of the various diversity concerns in search and recommendation,
highlighting their relationship and differences. For the survey's main body, we
present a unified taxonomy of diversification metrics and approaches in
retrieval systems, from both the search and recommendation perspectives. In the
later part of the survey, we discuss the open research questions of
diversity-aware research in search and recommendation in an effort to inspire
future innovations and encourage the implementation of diversity in real-world
systems.Comment: 20 page
Dynamic V2X Autonomous Perception from Road-to-Vehicle Vision
Vehicle-to-everything (V2X) perception is an innovative technology that
enhances vehicle perception accuracy, thereby elevating the security and
reliability of autonomous systems. However, existing V2X perception methods
focus on static scenes from mainly vehicle-based vision, which is constrained
by sensor capabilities and communication loads. To adapt V2X perception models
to dynamic scenes, we propose to build V2X perception from road-to-vehicle
vision and present Adaptive Road-to-Vehicle Perception (AR2VP) method. In
AR2VP,we leverage roadside units to offer stable, wide-range sensing
capabilities and serve as communication hubs. AR2VP is devised to tackle both
intra-scene and inter-scene changes. For the former, we construct a dynamic
perception representing module, which efficiently integrates vehicle
perceptions, enabling vehicles to capture a more comprehensive range of dynamic
factors within the scene.Moreover, we introduce a road-to-vehicle perception
compensating module, aimed at preserving the maximized roadside unit perception
information in the presence of intra-scene changes.For inter-scene changes, we
implement an experience replay mechanism leveraging the roadside unit's storage
capacity to retain a subset of historical scene data, maintaining model
robustness in response to inter-scene shifts. We conduct perception experiment
on 3D object detection and segmentation, and the results show that AR2VP excels
in both performance-bandwidth trade-offs and adaptability within dynamic
environments
Multi-Label Continual Learning using Augmented Graph Convolutional Network
Multi-Label Continual Learning (MLCL) builds a class-incremental framework in
a sequential multi-label image recognition data stream. The critical challenges
of MLCL are the construction of label relationships on past-missing and
future-missing partial labels of training data and the catastrophic forgetting
on old classes, resulting in poor generalization. To solve the problems, the
study proposes an Augmented Graph Convolutional Network (AGCN++) that can
construct the cross-task label relationships in MLCL and sustain catastrophic
forgetting. First, we build an Augmented Correlation Matrix (ACM) across all
seen classes, where the intra-task relationships derive from the hard label
statistics. In contrast, the inter-task relationships leverage hard and soft
labels from data and a constructed expert network. Then, we propose a novel
partial label encoder (PLE) for MLCL, which can extract dynamic class
representation for each partial label image as graph nodes and help generate
soft labels to create a more convincing ACM and suppress forgetting. Last, to
suppress the forgetting of label dependencies across old tasks, we propose a
relationship-preserving constrainter to construct label relationships. The
inter-class topology can be augmented automatically, which also yields
effective class representations. The proposed method is evaluated using two
multi-label image benchmarks. The experimental results show that the proposed
way is effective for MLCL image recognition and can build convincing
correlations across tasks even if the labels of previous tasks are missing