scholarly journals NeuRec: On Nonlinear Transformation for Personalized Ranking

2018 ◽  
Author(s):  
Shuai Zhang ◽  
Lina Yao ◽  
Aixin Sun ◽  
Sen Wang ◽  
Guodong Long ◽  
...  
Keyword(s):  
Neural Network ◽  
Real World ◽  
Real Life ◽  
Interaction Matrix ◽  
Interaction Patterns ◽  
Latent Factors ◽  
Integrated Network ◽  
Ranking Task ◽  
Real World Datasets ◽  
Personalized Ranking

Modeling user-item interaction patterns is an important task for personalized recommendations. Many recommender systems are based on the assumption that there exists a linear relationship between users and items while neglecting the intricacy and non-linearity of real-life historical interactions. In this paper, we propose a neural network based recommendation model (NeuRec) that untangles the complexity of user-item interactions and establish an integrated network to combine non-linear transformation with latent factors. We further design two variants of NeuRec: user-based NeuRec and item-based NeuRec, by focusing on different aspects of the interaction matrix. Extensive experiments on four real-world datasets demonstrated their superior performances on personalized ranking task.

scholarly journals Attention Enhanced Serial Unet++ Network for Removing Unevenly Distributed Haze

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2868
Author(s):  
Wenxuan Zhao ◽  
Yaqin Zhao ◽  
Liqi Feng ◽  
Jiaxi Tang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real World ◽  
Large Scale ◽  
Learning Strategy ◽  
Contextual Information ◽  
Small Scale ◽  
Image Dehazing ◽  
Atmospheric Scattering ◽  
Real World Datasets

The purpose of image dehazing is the reduction of the image degradation caused by suspended particles for supporting high-level visual tasks. Besides the atmospheric scattering model, convolutional neural network (CNN) has been used for image dehazing. However, the existing image dehazing algorithms are limited in face of unevenly distributed haze and dense haze in real-world scenes. In this paper, we propose a novel end-to-end convolutional neural network called attention enhanced serial Unet++ dehazing network (AESUnet) for single image dehazing. We attempt to build a serial Unet++ structure that adopts a serial strategy of two pruned Unet++ blocks based on residual connection. Compared with the simple Encoder–Decoder structure, the serial Unet++ module can better use the features extracted by encoders and promote contextual information fusion in different resolutions. In addition, we take some improvement measures to the Unet++ module, such as pruning, introducing the convolutional module with ResNet structure, and a residual learning strategy. Thus, the serial Unet++ module can generate more realistic images with less color distortion. Furthermore, following the serial Unet++ blocks, an attention mechanism is introduced to pay different attention to haze regions with different concentrations by learning weights in the spatial domain and channel domain. Experiments are conducted on two representative datasets: the large-scale synthetic dataset RESIDE and the small-scale real-world datasets I-HAZY and O-HAZY. The experimental results show that the proposed dehazing network is not only comparable to state-of-the-art methods for the RESIDE synthetic datasets, but also surpasses them by a very large margin for the I-HAZY and O-HAZY real-world dataset.

scholarly journals A Rating Prediction Recommendation Model Combined with the Optimizing Allocation for Information Granularity of Attributes

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 21
Author(s):  
Jianfei Li ◽  
Yongbin Wang ◽  
Zhulin Tao
Keyword(s):  
Neural Network ◽  
Latent Factors ◽  
Latent Factor ◽  
Information Granularity ◽  
Node Attribute ◽  
Item Attribute ◽  
Rating Prediction ◽  
Real World Datasets ◽  
Graph Neural Networks ◽  
Granularity Distribution

In recent years, graph neural networks (GNNS) have been demonstrated to be a powerful way to learn graph data. The existing recommender systems based on the implicit factor models mainly use the interactive information between users and items for training and learning. A user–item graph, a user–attribute graph, and an item–attribute graph are constructed according to the interactions between users and items. The latent factors of users and items can be learned in these graph structure data. There are many methods for learning the latent factors of users and items. Still, they do not fully consider the influence of node attribute information on the representation of the latent factors of users and items. We propose a rating prediction recommendation model, short for LNNSR, utilizing the level of information granularity allocated on each attribute by developing a granular neural network. The different granularity distribution proportion weights of each attribute can be learned in the granular neural network. The learned granularity allocation proportion weights are integrated into the latent factor representation of users and items. Thus, we can capture user-embedding representations and item-embedding representations more accurately, and it can also provide a reasonable explanation for the recommendation results. Finally, we concatenate the user latent factor-embedding and the item latent factor-embedding and then feed it into a multi-layer perceptron for rating prediction. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework.

scholarly journals Selecting a Cost-Effective Seed for Maximizing the Social Influence Under Real-life Constraints

2021 ◽  
Author(s):  
Tarun Kumer Biswas
Keyword(s):  
Real World ◽  
Seed Set ◽  
Real Life ◽  
Optimal Solution ◽  
Cost Effective ◽  
Influence Maximization ◽  
Cardinality Constraints ◽  
De Algorithm ◽  
Real World Datasets ◽  
Simple Additive Weighting

The Influence Maximization (IM) problem aims at maximizing the diffusion of information or adoption of products among users in a social network by identifying and activating a set of initial users. In real-life applications, it is not unrealistic to have a higher activation cost for a user with higher influence. However, the existing works on IM consider finding the most influential users as the seed set, ignoring either the activation costs of such individual nodes and the total budget or the size of the seed set, which may not be always an optimal solution, particularly from the financial and managerial perspectives, respectively. To address these issues, we propose a more realistic and generalized formulation termed as multi-constraint influence maximization (MCIM) aiming to achieve a cost-effective solution under both budgetary and cardinality constraints. Unlike the existing IM formulations, the proposed MCIM is no longer a monotone but a submodular function. As it is also proved to be an NP-hard problem, we propose a simple additive weighting (SAW) assisted differential evolution (DE) algorithm for solving the large-size real-world problems. Experimental results on four real-world datasets show that the proposed formulation and algorithm are effective in finding a cost-effective seed set.

scholarly journals Stabilizing Adversarial Invariance Induction from Divergence Minimization Perspective

2020 ◽  
Author(s):  
Yusuke Iwasawa ◽  
Kei Akuzawa ◽  
Yutaka Matsuo
Keyword(s):  
Neural Network ◽  
Real World ◽  
Optimization Procedure ◽  
Superior Performance ◽  
Simple Variant ◽  
Invariant Representations ◽  
Real World Datasets ◽  
Divergence Minimization

Adversarial invariance induction (AII) is a generic and powerful framework for enforcing an invariance to nuisance attributes into neural network representations. However, its optimization is often unstable and little is known about its practical behavior. This paper presents an analysis of the reasons for the optimization difficulties and provides a better optimization procedure by rethinking AII from a divergence minimization perspective. Interestingly, this perspective indicates a cause of the optimization difficulties: it does not ensure proper divergence minimization, which is a requirement of the invariant representations. We then propose a simple variant of AII, called invariance induction by discriminator matching, which takes into account the divergence minimization interpretation of the invariant representations. Our method consistently achieves near-optimal invariance in toy datasets with various configurations in which the original AII is catastrophically unstable. Extentive experiments on four real-world datasets also support the superior performance of the proposed method, leading to improved user anonymization and domain generalization.

Deep Multi-Instance Multi-Label Learning for Image Annotation

2017 ◽  
Vol 32 (03) ◽  
pp. 1859005 ◽  
Author(s):  
Hai-Feng Guo ◽  
Lixin Han ◽  
Shoubao Su ◽  
Zhou-Bao Sun
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real World ◽  
Supervised Classification ◽  
Image Annotation ◽  
Learning Framework ◽  
Recent Advancement ◽  
Real World Datasets

Multi-Instance Multi-Label learning (MIML) is a popular framework for supervised classification where an example is described by multiple instances and associated with multiple labels. Previous MIML approaches have focused on predicting labels for instances. The idea of tackling the problem is to identify its equivalence in the traditional supervised learning framework. Motivated by the recent advancement in deep learning, in this paper, we still consider the problem of predicting labels and attempt to model deep learning in MIML learning framework. The proposed approach enables us to train deep convolutional neural network with images from social networks where images are well labeled, even labeled with several labels or uncorrelated labels. Experiments on real-world datasets demonstrate the effectiveness of our proposed approach.

scholarly journals Cross-Domain Recommendation via Coupled Factorization Machines

2019 ◽  
Vol 33 ◽  
pp. 9965-9966 ◽  
Author(s):  
Lile Li ◽  
Quan Do ◽  
Wei Liu
Keyword(s):  
Recommender Systems ◽  
Real World ◽  
Original Data ◽  
Data Sets ◽  
Latent Factors ◽  
Domain Specific ◽  
Coupled Mode ◽  
Cross Domain ◽  
Factorization Machine ◽  
Real World Datasets

Data across many business domains can be represented by two or more coupled data sets. Correlations among these coupled datasets have been studied in the literature for making more accurate cross-domain recommender systems. However, in existing methods, cross-domain recommendations mostly assume the coupled mode of data sets share identical latent factors, which limits the discovery of potentially useful domain-specific properties of the original data. In this paper, we proposed a novel cross-domain recommendation method called Coupled Factorization Machine (CoFM) that addresses this limitation. Compared to existing models, our research is the first model that uses factorization machines to capture both common characteristics of coupled domains while simultaneously preserving the differences among them. Our experiments with real-world datasets confirm the advantages of our method in making across-domain recommendations.

scholarly journals An Attentional Recurrent Neural Network for Personalized Next Location Recommendation

2020 ◽  
Vol 34 (01) ◽  
pp. 83-90
Author(s):  
Qing Guo ◽  
Zhu Sun ◽  
Jie Zhang ◽  
Yin-Leng Theng
Keyword(s):  
Neural Network ◽  
Random Walk ◽  
Recurrent Neural Network ◽  
Real World ◽  
State Of The Art ◽  
Knowledge Graph ◽  
User Mobility ◽  
Location Recommendation ◽  
Meta Path ◽  
Real World Datasets

Most existing studies on next location recommendation propose to model the sequential regularity of check-in sequences, but suffer from the severe data sparsity issue where most locations have fewer than five following locations. To this end, we propose an Attentional Recurrent Neural Network (ARNN) to jointly model both the sequential regularity and transition regularities of similar locations (neighbors). In particular, we first design a meta-path based random walk over a novel knowledge graph to discover location neighbors based on heterogeneous factors. A recurrent neural network is then adopted to model the sequential regularity by capturing various contexts that govern user mobility. Meanwhile, the transition regularities of the discovered neighbors are integrated via the attention mechanism, which seamlessly cooperates with the sequential regularity as a unified recurrent framework. Experimental results on multiple real-world datasets demonstrate that ARNN outperforms state-of-the-art methods.

scholarly journals Metric Factorization with Item Cooccurrence for Recommendation

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 512 ◽  
Author(s):  
Honglin Dai ◽  
Liejun Wang ◽  
Jiwei Qin
Keyword(s):  
Matrix Factorization ◽  
Euclidean Distance ◽  
Metric Learning ◽  
Sparse Data ◽  
Inner Product ◽  
Interaction Matrix ◽  
Latent Factors ◽  
Rating Prediction ◽  
Real World Datasets ◽  
Item Item

In modern recommender systems, matrix factorization has been widely used to decompose the user–item matrix into user and item latent factors. However, the inner product in matrix factorization does not satisfy the triangle inequality, and the problem of sparse data is also encountered. In this paper, we propose a novel recommendation model, namely, metric factorization with item cooccurrence for recommendation (MFIC), which uses the Euclidean distance to jointly decompose the user–item interaction matrix and the item–item cooccurrence with shared latent factors. The item cooccurrence matrix is obtained from the colike matrix through the calculation of pointwise mutual information. The main contributions of this paper are as follows: (1) The MFIC model is not only suitable for rating prediction and item ranking, but can also well overcome the problem of sparse data. (2) This model incorporates the item cooccurrence matrix into metric learning so it can better learn the spatial positions of users and items. (3) Extensive experiments on a number of real-world datasets show that the proposed method substantially outperforms the compared algorithm in both rating prediction and item ranking.

Distributed Latent Dirichlet Allocation on Streams

2021 ◽  
Vol 16 (1) ◽  
pp. 1-20
Author(s):  
Yunyan Guo ◽  
Jianzhong Li
Keyword(s):  
Real Time ◽  
Language Processing ◽  
Real World ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Real Life ◽  
Streaming Data ◽  
Real World Datasets ◽  
Dirichlet Allocation ◽  
Online Inference

Latent Dirichlet Allocation (LDA) has been widely used for topic modeling, with applications spanning various areas such as natural language processing and information retrieval. While LDA on small and static datasets has been extensively studied, several real-world challenges are posed in practical scenarios where datasets are often huge and are gathered in a streaming fashion. As the state-of-the-art LDA algorithm on streams, Streaming Variational Bayes (SVB) introduced Bayesian updating to provide a streaming procedure. However, the utility of SVB is limited in applications since it ignored three challenges of processing real-world streams: topic evolution , data turbulence , and real-time inference . In this article, we propose a novel distributed LDA algorithm—referred to as StreamFed-LDA— to deal with challenges on streams. For topic modeling of streaming data, the ability to capture evolving topics is essential for practical online inference. To achieve this goal, StreamFed-LDA is based on a specialized framework that supports lifelong (continual) learning of evolving topics. On the other hand, data turbulence is commonly present in streams due to real-life events. In that case, the design of StreamFed-LDA allows the model to learn new characteristics from the most recent data while maintaining the historical information. On massive streaming data, it is difficult and crucial to provide real-time inference results. To increase the throughput and reduce the latency, StreamFed-LDA introduces additional techniques that substantially reduce both computation and communication costs in distributed systems. Experiments on four real-world datasets show that the proposed framework achieves significantly better performance of online inference compared with the baselines. At the same time, StreamFed-LDA also reduces the latency by orders of magnitudes in real-world datasets.

scholarly journals Selecting a Cost-Effective Seed for Maximizing the Social Influence Under Real-life Constraints

2021 ◽  
Author(s):  
Tarun Kumer Biswas
Keyword(s):  
Real World ◽  
Seed Set ◽  
Real Life ◽  
Optimal Solution ◽  
Cost Effective ◽  
Influence Maximization ◽  
Cardinality Constraints ◽  
De Algorithm ◽  
Real World Datasets ◽  
Simple Additive Weighting

The Influence Maximization (IM) problem aims at maximizing the diffusion of information or adoption of products among users in a social network by identifying and activating a set of initial users. In real-life applications, it is not unrealistic to have a higher activation cost for a user with higher influence. However, the existing works on IM consider finding the most influential users as the seed set, ignoring either the activation costs of such individual nodes and the total budget or the size of the seed set, which may not be always an optimal solution, particularly from the financial and managerial perspectives, respectively. To address these issues, we propose a more realistic and generalized formulation termed as multi-constraint influence maximization (MCIM) aiming to achieve a cost-effective solution under both budgetary and cardinality constraints. Unlike the existing IM formulations, the proposed MCIM is no longer a monotone but a submodular function. As it is also proved to be an NP-hard problem, we propose a simple additive weighting (SAW) assisted differential evolution (DE) algorithm for solving the large-size real-world problems. Experimental results on four real-world datasets show that the proposed formulation and algorithm are effective in finding a cost-effective seed set.

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