30 research outputs found
Network On Network for Tabular Data Classification in Real-world Applications
Tabular data is the most common data format adopted by our customers ranging
from retail, finance to E-commerce, and tabular data classification plays an
essential role to their businesses. In this paper, we present Network On
Network (NON), a practical tabular data classification model based on deep
neural network to provide accurate predictions. Various deep methods have been
proposed and promising progress has been made. However, most of them use
operations like neural network and factorization machines to fuse the
embeddings of different features directly, and linearly combine the outputs of
those operations to get the final prediction. As a result, the intra-field
information and the non-linear interactions between those operations (e.g.
neural network and factorization machines) are ignored. Intra-field information
is the information that features inside each field belong to the same field.
NON is proposed to take full advantage of intra-field information and
non-linear interactions. It consists of three components: field-wise network at
the bottom to capture the intra-field information, across field network in the
middle to choose suitable operations data-drivenly, and operation fusion
network on the top to fuse outputs of the chosen operations deeply. Extensive
experiments on six real-world datasets demonstrate NON can outperform the
state-of-the-art models significantly. Furthermore, both qualitative and
quantitative study of the features in the embedding space show NON can capture
intra-field information effectively
STGIN: Spatial-Temporal Graph Interaction Network for Large-scale POI Recommendation
In Location-Based Services, Point-Of-Interest(POI) recommendation plays a
crucial role in both user experience and business opportunities. Graph neural
networks have been proven effective in providing personalized POI
recommendation services. However, there are still two critical challenges.
First, existing graph models attempt to capture users' diversified interests
through a unified graph, which limits their ability to express interests in
various spatial-temporal contexts. Second, the efficiency limitations of graph
construction and graph sampling in large-scale systems make it difficult to
adapt quickly to new real-time interests. To tackle the above challenges, we
propose a novel Spatial-Temporal Graph Interaction Network. Specifically, we
construct subgraphs of spatial, temporal, spatial-temporal, and global views
respectively to precisely characterize the user's interests in various
contexts. In addition, we design an industry-friendly framework to track the
user's latest interests. Extensive experiments on the real-world dataset show
that our method outperforms state-of-the-art models. This work has been
successfully deployed in a large e-commerce platform, delivering a 1.1% CTR and
6.3% RPM improvement.Comment: accepted by CIKM 202
AdaptDHM: Adaptive Distribution Hierarchical Model for Multi-Domain CTR Prediction
Large-scale commercial platforms usually involve numerous business domains
for diverse business strategies and expect their recommendation systems to
provide click-through rate (CTR) predictions for multiple domains
simultaneously. Existing promising and widely-used multi-domain models discover
domain relationships by explicitly constructing domain-specific networks, but
the computation and memory boost significantly with the increase of domains. To
reduce computational complexity, manually grouping domains with particular
business strategies is common in industrial applications. However, this
pre-defined data partitioning way heavily relies on prior knowledge, and it may
neglect the underlying data distribution of each domain, hence limiting the
model's representation capability. Regarding the above issues, we propose an
elegant and flexible multi-distribution modeling paradigm, named Adaptive
Distribution Hierarchical Model (AdaptDHM), which is an end-to-end optimization
hierarchical structure consisting of a clustering process and classification
process. Specifically, we design a distribution adaptation module with a
customized dynamic routing mechanism. Instead of introducing prior knowledge
for pre-defined data allocation, this routing algorithm adaptively provides a
distribution coefficient for each sample to determine which cluster it belongs
to. Each cluster corresponds to a particular distribution so that the model can
sufficiently capture the commonalities and distinctions between these distinct
clusters. Extensive experiments on both public and large-scale Alibaba
industrial datasets verify the effectiveness and efficiency of AdaptDHM: Our
model achieves impressive prediction accuracy and its time cost during the
training stage is more than 50% less than that of other models
KAST: Knowledge Aware Adaptive Session Multi-Topic Network for Click-Through Rate Prediction
Capturing the evolving trends of user interest is important for both
recommendation systems and advertising systems, and user behavior sequences
have been successfully used in Click-Through-Rate(CTR) prediction problems.
However, if the user interest is learned on the basis of item-level behaviors,
the performance may be affected by the following two issues. Firstly, some
casual outliers might be included in the behavior sequences as user behaviors
are likely to be diverse. Secondly, the span of time intervals between user
behaviors is random and irregular, for which a RNN-based module employed from
NLP is not perfectly adaptive. To handle these two issues, we propose the
Knowledge aware Adaptive Session multi-Topic network(KAST). It can adaptively
segment user sessions from the whole user behavior sequence, and maintain
similar intents in the same session. Furthermore, in order to improve the
quality of session segmentation and representation, a knowledge-aware module is
introduced so that the structural information from the user-item interaction
can be extracted in an end-to-end manner, and a marginal based loss with these
information is merged into the major loss. Through extensive experiments on
public benchmarks, we demonstrate that KAST can achieve superior performance
than state-of-the-art methods for CTR prediction, and key modules and
hyper-parameters are also evaluated
MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction
Click-through rate (CTR) prediction is a critical task for many industrial
systems, such as display advertising and recommender systems. Recently,
modeling user behavior sequences attracts much attention and shows great
improvements in the CTR field. Existing works mainly exploit attention
mechanism based on embedding product when considering relations between user
behaviors and target item. However, this methodology lacks of concrete
semantics and overlooks the underlying reasons driving a user to click on a
target item. In this paper, we propose a new framework named Multiplex
Target-Behavior Relation enhanced Network (MTBRN) to leverage multiplex
relations between user behaviors and target item to enhance CTR prediction.
Multiplex relations consist of meaningful semantics, which can bring a better
understanding on users' interests from different perspectives. To explore and
model multiplex relations, we propose to incorporate various graphs (e.g.,
knowledge graph and item-item similarity graph) to construct multiple
relational paths between user behaviors and target item. Then Bi-LSTM is
applied to encode each path in the path extractor layer. A path fusion network
and a path activation network are devised to adaptively aggregate and finally
learn the representation of all paths for CTR prediction. Extensive offline and
online experiments clearly verify the effectiveness of our framework.Comment: Accepted by CIKM202