scholarly journals Exploiting Meta-Path with Attention Mechanism for Fine-Grained User Location Prediction in LBSNs

2019 ◽  
Vol 1284 ◽  
pp. 012031
Author(s):  
Zhixiao Wang ◽  
Wenyao Yan ◽  
Wendong Wang ◽  
Ang Gao ◽  
Lei Yu ◽  
...  
Keyword(s):  
Attention Mechanism ◽  
Location Prediction ◽  
Fine Grained ◽  
Meta Path ◽  
User Location

Fine-Grained User Location Prediction using Meta-Path Context with Attention Mechanism

2021 ◽  
Author(s):  
Zhixiao Wang ◽  
Wenyao Yan ◽  
Ang Gao
Keyword(s):  
Social Networks ◽  
Positive Sample ◽  
Data Sets ◽  
Suggested Approach ◽  
Heterogeneous Information ◽  
Fine Grained ◽  
High Efficient ◽  
Meta Path ◽  
Location Based Social Networks ◽  
User Location

The prevalence of Location-Based Social Networks (LBSNs) significantly improves the location-aware capability of services by providing Geo-tagged information. Relied on a great number of user check-in data in the location-based social networks, their essential mobility modes are able to be comprehensively studied, which is basic for forecasting the next venue where a specific user is going to visit considering his relevant historical check-in data. Since there exist different kinds of nodes and interactions between nodes, these information could be look upon as a network that is made up of heterogeneous information. In this network a few of different semantic meta paths could be obtained. Enlightened from the competitive advantage of embedding method relied upon meta-path contexts in the heterogeneous information network, we study a joint deep learning scheme exploring different meta-path context information to forecast fine-grained location. In order to capture different semantics in a user-location interaction, we adopt a simple but high-efficient attention method to learn the meta-path importance or weights. In the terms of model optimization, considering we have only positive sample data and there exists intrinsically latent feedback in check-in information, herein a pairwise learning method is utilized for maximizing the margin between visited and invisible venues. Experiment in different data-sets validate the competitive performance of the suggested approach under different assessment criterion.

scholarly journals Venue2Vec: An Efficient Embedding Model for Fine-Grained User Location Prediction in Geo-Social Networks

2020 ◽  
Vol 14 (2) ◽  
pp. 1740-1751 ◽  
Author(s):  
Shuai Xu ◽  
Jiuxin Cao ◽  
Phil Legg ◽  
Bo Liu ◽  
Shancang Li
Keyword(s):  
Social Networks ◽  
Location Prediction ◽  
Fine Grained ◽  
User Location

scholarly journals Person Reidentification Model Based on Multiattention Modules and Multiscale Residuals

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yongyi Li ◽  
Shiqi Wang ◽  
Shuang Dong ◽  
Xueling Lv ◽  
Changzhi Lv ◽  
...  
Keyword(s):  
Local Features ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Original Network ◽  
Fine Grained ◽  
Backbone Network ◽  
Model Based ◽  
Local Branch ◽  
Feature Expression ◽  
Global And Local

At present, person reidentification based on attention mechanism has attracted many scholars’ interests. Although attention module can improve the representation ability and reidentification accuracy of Re-ID model to a certain extent, it depends on the coupling of attention module and original network. In this paper, a person reidentification model that combines multiple attentions and multiscale residuals is proposed. The model introduces combined attention fusion module and multiscale residual fusion module in the backbone network ResNet 50 to enhance the feature flow between residual blocks and better fuse multiscale features. Furthermore, a global branch and a local branch are designed and applied to enhance the channel aggregation and position perception ability of the network by utilizing the dual ensemble attention module, as along as the fine-grained feature expression is obtained by using multiproportion block and reorganization. Thus, the global and local features are enhanced. The experimental results on Market-1501 dataset and DukeMTMC-reID dataset show that the indexes of the presented model, especially Rank-1 accuracy, reach 96.20% and 89.59%, respectively, which can be considered as a progress in Re-ID.

Federated Meta-Location Learning for Fine-Grained Location Prediction

2021 ◽  
Author(s):  
Xiaopeng Jiang ◽  
Shuai Zhao ◽  
Guy Jacobson ◽  
Rittwik Jana ◽  
Wen-Ling Hsu ◽  
...  
Keyword(s):  
Location Prediction ◽  
Fine Grained

scholarly journals Attend and Rectify: A Gated Attention Mechanism for Fine-Grained Recovery

2018 ◽  
pp. 357-372 ◽  
Author(s):  
Pau Rodríguez ◽  
Josep M. Gonfaus ◽  
Guillem Cucurull ◽  
F. Xavier Roca ◽  
Jordi Gonzàlez
Keyword(s):  
Attention Mechanism ◽  
Fine Grained

scholarly journals Hybrid Attention Network for Language-Based Person Search

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5279
Author(s):  
Yang Li ◽  
Huahu Xu ◽  
Junsheng Xiao
Keyword(s):  
Image Features ◽  
Attention Mechanism ◽  
Feature Representation ◽  
Semantic Features ◽  
Retrieval Task ◽  
Attention Network ◽  
Fine Grained ◽  
Person Search ◽  
High Level ◽  
Language Description

Language-based person search retrieves images of a target person using natural language description and is a challenging fine-grained cross-modal retrieval task. A novel hybrid attention network is proposed for the task. The network includes the following three aspects: First, a cubic attention mechanism for person image, which combines cross-layer spatial attention and channel attention. It can fully excavate both important midlevel details and key high-level semantics to obtain better discriminative fine-grained feature representation of a person image. Second, a text attention network for language description, which is based on bidirectional LSTM (BiLSTM) and self-attention mechanism. It can better learn the bidirectional semantic dependency and capture the key words of sentences, so as to extract the context information and key semantic features of the language description more effectively and accurately. Third, a cross-modal attention mechanism and a joint loss function for cross-modal learning, which can pay more attention to the relevant parts between text and image features. It can better exploit both the cross-modal and intra-modal correlation and can better solve the problem of cross-modal heterogeneity. Extensive experiments have been conducted on the CUHK-PEDES dataset. Our approach obtains higher performance than state-of-the-art approaches, demonstrating the advantage of the approach we propose.

scholarly journals Cash-Out User Detection Based on Attributed Heterogeneous Information Network with a Hierarchical Attention Mechanism

2019 ◽  
Vol 33 ◽  
pp. 946-953 ◽  
Author(s):  
Binbin Hu ◽  
Zhiqiang Zhang ◽  
Chuan Shi ◽  
Jun Zhou ◽  
Xiaolong Li ◽  
...  
Keyword(s):  
Financial Services ◽  
Attention Mechanism ◽  
Feature Representation ◽  
Information Network ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Structure Information ◽  
Detection Model ◽  
Meta Path ◽  
Interaction Relations

As one of the major frauds in financial services, cash-out fraud is that users pursue cash gains with illegal or insincere means. Conventional solutions for the cash-out user detection are to perform subtle feature engineering for each user and then apply a classifier, such as GDBT and Neural Network. However, users in financial services have rich interaction relations, which are seldom fully exploited by conventional solutions. In this paper, with the real datasets in Ant Credit Pay of Ant Financial Services Group, we first study the cashout user detection problem and propose a novel hierarchical attention mechanism based cash-out user detection model, called HACUD. Specifically, we model different types of objects and their rich attributes and interaction relations in the scenario of credit payment service with an Attributed Heterogeneous Information Network (AHIN). The HACUD model enhances feature representation of objects through meta-path based neighbors exploiting different aspects of structure information in AHIN. Furthermore, a hierarchical attention mechanism is elaborately designed to model user’s preferences towards attributes and meta-paths. Experimental results on two real datasets show that the HACUD outperforms the state-of-the-art methods.

scholarly journals Earlier Attention? Aspect-Aware LSTM for Aspect-Based Sentiment Analysis

2019 ◽  
Author(s):  
Bowen Xing ◽  
Lejian Liao ◽  
Dandan Song ◽  
Jingang Wang ◽  
Fuzheng Zhang ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Irrelevant Information ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Context Modeling ◽  
Fine Grained ◽  
Related Information ◽  
Modeling Process ◽  
Novel Variant

Aspect-based sentiment analysis (ABSA) aims to predict fine-grained sentiments of comments with respect to given aspect terms or categories. In previous ABSA methods, the importance of aspect has been realized and verified. Most existing LSTM-based models take aspect into account via the attention mechanism, where the attention weights are calculated after the context is modeled in the form of contextual vectors. However, aspect-related information may be already discarded and aspect-irrelevant information may be retained in classic LSTM cells in the context modeling process, which can be improved to generate more effective context representations. This paper proposes a novel variant of LSTM, termed as aspect-aware LSTM (AA-LSTM), which incorporates aspect information into LSTM cells in the context modeling stage before the attention mechanism. Therefore, our AA-LSTM can dynamically produce aspect-aware contextual representations. We experiment with several representative LSTM-based models by replacing the classic LSTM cells with the AA-LSTM cells. Experimental results on SemEval-2014 Datasets demonstrate the effectiveness of AA-LSTM.

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