786 research outputs found
Interaction-aware Factorization Machines for Recommender Systems
Factorization Machine (FM) is a widely used supervised learning approach by
effectively modeling of feature interactions. Despite the successful
application of FM and its many deep learning variants, treating every feature
interaction fairly may degrade the performance. For example, the interactions
of a useless feature may introduce noises; the importance of a feature may also
differ when interacting with different features. In this work, we propose a
novel model named \emph{Interaction-aware Factorization Machine} (IFM) by
introducing Interaction-Aware Mechanism (IAM), which comprises the
\emph{feature aspect} and the \emph{field aspect}, to learn flexible
interactions on two levels. The feature aspect learns feature interaction
importance via an attention network while the field aspect learns the feature
interaction effect as a parametric similarity of the feature interaction vector
and the corresponding field interaction prototype. IFM introduces more
structured control and learns feature interaction importance in a stratified
manner, which allows for more leverage in tweaking the interactions on both
feature-wise and field-wise levels. Besides, we give a more generalized
architecture and propose Interaction-aware Neural Network (INN) and DeepIFM to
capture higher-order interactions. To further improve both the performance and
efficiency of IFM, a sampling scheme is developed to select interactions based
on the field aspect importance. The experimental results from two well-known
datasets show the superiority of the proposed models over the state-of-the-art
methods
CoNet: Collaborative Cross Networks for Cross-Domain Recommendation
The cross-domain recommendation technique is an effective way of alleviating
the data sparse issue in recommender systems by leveraging the knowledge from
relevant domains. Transfer learning is a class of algorithms underlying these
techniques. In this paper, we propose a novel transfer learning approach for
cross-domain recommendation by using neural networks as the base model. In
contrast to the matrix factorization based cross-domain techniques, our method
is deep transfer learning, which can learn complex user-item interaction
relationships. We assume that hidden layers in two base networks are connected
by cross mappings, leading to the collaborative cross networks (CoNet). CoNet
enables dual knowledge transfer across domains by introducing cross connections
from one base network to another and vice versa. CoNet is achieved in
multi-layer feedforward networks by adding dual connections and joint loss
functions, which can be trained efficiently by back-propagation. The proposed
model is thoroughly evaluated on two large real-world datasets. It outperforms
baselines by relative improvements of 7.84\% in NDCG. We demonstrate the
necessity of adaptively selecting representations to transfer. Our model can
reduce tens of thousands training examples comparing with non-transfer methods
and still has the competitive performance with them.Comment: Deep transfer learning for recommender system
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
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
Mobile app recommendations using deep learning and big data
Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRMRecommender systems were first introduced to solve information overload problems in enterprises. Over the last decades, recommender systems have found applications in several major websites related to e-commerce, music and video streaming, travel and movie sites, social media and mobile app stores. Several methods have been proposed over the years to build recommender systems. The most popular approaches are based on collaborative filtering techniques, which leverage the similarities between consumer tastes. But the current state of the art in recommender systems is deep-learning methods, which can leverage not only item consumption data but also content, context, and user attributes. Mobile app stores generate data with Big Data properties from app consumption data, behavioral, geographic, demographic, social network and user-generated content data, which includes reviews, comments and search queries. In this dissertation, we propose a deep-learning architecture for recommender systems in mobile app stores that leverage most of these data sources. We analyze three issues related to the impact of the data sources, the impact of embedding layer pretraining and the efficiency of using Kernel methods to improve app scoring at a Big Data scale. An experiment is conducted on a Portuguese Android app store. Results suggest that models can be improved by combining structured and unstructured data. The results also suggest that embedding layer pretraining is essential to obtain good results. Some evidence is provided showing that Kernel-based methods might not be efficient when deployed in Big Data contexts
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