1,743 research outputs found
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
Distributed Equivalent Substitution Training for Large-Scale Recommender Systems
We present Distributed Equivalent Substitution (DES) training, a novel
distributed training framework for large-scale recommender systems with dynamic
sparse features. DES introduces fully synchronous training to large-scale
recommendation system for the first time by reducing communication, thus making
the training of commercial recommender systems converge faster and reach better
CTR. DES requires much less communication by substituting the weights-rich
operators with the computationally equivalent sub-operators and aggregating
partial results instead of transmitting the huge sparse weights directly
through the network. Due to the use of synchronous training on large-scale Deep
Learning Recommendation Models (DLRMs), DES achieves higher AUC(Area Under
ROC). We successfully apply DES training on multiple popular DLRMs of
industrial scenarios. Experiments show that our implementation outperforms the
state-of-the-art PS-based training framework, achieving up to 68.7%
communication savings and higher throughput compared to other PS-based
recommender systems.Comment: Accepted by SIGIR '2020. Proceedings of the 43rd International ACM
SIGIR Conference on Research and Development in Information Retrieval. 202
M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems
Combining graph representation learning with multi-view data (side
information) for recommendation is a trend in industry. Most existing methods
can be categorized as \emph{multi-view representation fusion}; they first build
one graph and then integrate multi-view data into a single compact
representation for each node in the graph. However, these methods are raising
concerns in both engineering and algorithm aspects: 1) multi-view data are
abundant and informative in industry and may exceed the capacity of one single
vector, and 2) inductive bias may be introduced as multi-view data are often
from different distributions. In this paper, we use a \emph{multi-view
representation alignment} approach to address this issue. Particularly, we
propose a multi-task multi-view graph representation learning framework (M2GRL)
to learn node representations from multi-view graphs for web-scale recommender
systems. M2GRL constructs one graph for each single-view data, learns multiple
separate representations from multiple graphs, and performs alignment to model
cross-view relations. M2GRL chooses a multi-task learning paradigm to learn
intra-view representations and cross-view relations jointly. Besides, M2GRL
applies homoscedastic uncertainty to adaptively tune the loss weights of tasks
during training. We deploy M2GRL at Taobao and train it on 57 billion examples.
According to offline metrics and online A/B tests, M2GRL significantly
outperforms other state-of-the-art algorithms. Further exploration on diversity
recommendation in Taobao shows the effectiveness of utilizing multiple
representations produced by \method{}, which we argue is a promising direction
for various industrial recommendation tasks of different focus.Comment: Accepted by KDD 2020 ads track as an oral paper. Code
address:https://github.com/99731/M2GR
Reviewing Developments of Graph Convolutional Network Techniques for Recommendation Systems
The Recommender system is a vital information service on today's Internet.
Recently, graph neural networks have emerged as the leading approach for
recommender systems. We try to review recent literature on graph neural
network-based recommender systems, covering the background and development of
both recommender systems and graph neural networks. Then categorizing
recommender systems by their settings and graph neural networks by spectral and
spatial models, we explore the motivation behind incorporating graph neural
networks into recommender systems. We also analyze challenges and open problems
in graph construction, embedding propagation and aggregation, and computation
efficiency. This guides us to better explore the future directions and
developments in this domain.Comment: arXiv admin note: text overlap with arXiv:2103.08976 by other author
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
- …