scholarly journals A Joint Deep Recommendation Framework for Location-Based Social Networks

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Omer Tal ◽  
Yang Liu
Keyword(s):  
Neural Networks ◽  
Social Networks ◽  
Deep Learning ◽  
Spatial Data ◽  
Mean Squared Error ◽  
Multilayer Perceptrons ◽  
Point Of Interest ◽  
Exciting Opportunity ◽  
Multiple State ◽  
Location Based Social Networks

Location-based social networks, such as Yelp and Tripadvisor, which allow users to share experiences about visited locations with their friends, have gained increasing popularity in recent years. However, as more locations become available, the need for accurate systems able to present personalized suggestions arises. By providing such service, point-of-interest recommender systems have attracted much interest from different societies, leading to improved methods and techniques. Deep learning provides an exciting opportunity to further enhance these systems, by utilizing additional data to understand users’ preferences better. In this work we propose Textual and Contextual Embedding-based Neural Recommender (TCENR), a deep framework that employs contextual data, such as users’ social networks and locations’ geo-spatial data, along with textual reviews. To make best use of these inputs, we utilize multiple types of deep neural networks that are best suited for each type of data. TCENR adopts the popular multilayer perceptrons to analyze historical activities in the system, while the learning of textual reviews is achieved using two variations of the suggested framework. One is based on convolutional neural networks to extract meaningful data from textual reviews, and the other employs recurrent neural networks. Our proposed network is evaluated over the Yelp dataset and found to outperform multiple state-of-the-art baselines in terms of accuracy, mean squared error, precision, and recall. In addition, we provide further insight into the design selections and hyperparameters of our recommender system, hoping to shed light on the benefit of deep learning for location-based social network recommendation.

scholarly journals POI Recommendation Method Using Deep Learning in Location-Based Social Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yang Liu ◽  
An-bo Wu
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Short Term Memory ◽  
Large Data ◽  
Short Term ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Bayesian Personalized Ranking ◽  
Location Based Social Networks ◽  
Current Sequence

To solve the problems of large data sparsity and lack of negative samples in most point of interest (POI) recommendation methods, a POI recommendation method based on deep learning in location-based social networks is proposed. Firstly, a bidirectional long-short-term memory (Bi-LSTM) attention mechanism is designed to give different weights to different parts of the current sequence according to users’ long-term and short-term preferences. Then, the POI recommendation model is constructed, the sequence state data of the encoder is input into Bi-LSTM-Attention to get the attention representation of the current POI check-in sequence, and the Top- N recommendation list is generated after the decoder processing. Finally, a negative sampling method is proposed to obtain an effective negative sample set, which is used to improve the calculation of the Bayesian personalized ranking loss function. The proposed method is demonstrated experimentally on Foursquare and Gowalla datasets. The experimental results show that the proposed method has better accuracy, recall, and F1 value than other comparison methods.

scholarly journals Point-of-Interest Recommender Systems based on Location-Based Social Networks: A Survey from an Experimental Perspective

2022 ◽  
Author(s):  
Pablo Sánchez ◽  
Alejandro Bellogín
Keyword(s):  
Social Networks ◽  
Recommender Systems ◽  
Information Sources ◽  
Learning Approaches ◽  
Performance Improvements ◽  
Point Of Interest ◽  
Real Performance ◽  
Research Questions ◽  
Developing Area ◽  
Location Based Social Networks

Point-of-Interest recommendation is an increasing research and developing area within the widely adopted technologies known as Recommender Systems. Among them, those that exploit information coming from Location-Based Social Networks (LBSNs) are very popular nowadays and could work with different information sources, which pose several challenges and research questions to the community as a whole. We present a systematic review focused on the research done in the last 10 years about this topic. We discuss and categorize the algorithms and evaluation methodologies used in these works and point out the opportunities and challenges that remain open in the field. More specifically, we report the leading recommendation techniques and information sources that have been exploited more often (such as the geographical signal and deep learning approaches) while we also alert about the lack of reproducibility in the field that may hinder real performance improvements.

DeepScan: Exploiting Deep Learning for Malicious Account Detection in Location-Based Social Networks

2018 ◽  
Vol 56 (11) ◽  
pp. 21-27 ◽  
Author(s):  
Qingyuan Gong ◽  
Yang Chen ◽  
Xinlei He ◽  
Zhou Zhuang ◽  
Tianyi Wang ◽  
...  
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Location Based Social Networks

scholarly journals A Guide for Using Deep Learning for Complex Trait Genomic Prediction

Genes ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 553 ◽  
Author(s):  
Pérez-Enciso ◽  
Zingaretti
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Genomic Prediction ◽  
Genetic Risk ◽  
Complex Trait ◽  
Molecular Data ◽  
Multilayer Perceptrons ◽  
Complex Data ◽  
Theoretical Foundations

Deep learning (DL) has emerged as a powerful tool to make accurate predictions from complex data such as image, text, or video. However, its ability to predict phenotypic values from molecular data is less well studied. Here, we describe the theoretical foundations of DL and provide a generic code that can be easily modified to suit specific needs. DL comprises a wide variety of algorithms which depend on numerous hyperparameters. Careful optimization of hyperparameter values is critical to avoid overfitting. Among the DL architectures currently tested in genomic prediction, convolutional neural networks (CNNs) seem more promising than multilayer perceptrons (MLPs). A limitation of DL is in interpreting the results. This may not be relevant for genomic prediction in plant or animal breeding but can be critical when deciding the genetic risk to a disease. Although DL technologies are not ”plug-and-play”, they are easily implemented using Keras and TensorFlow public software. To illustrate the principles described here, we implemented a Keras-based code in GitHub.

scholarly journals Mobile User Location Inference Attacks Fusing with Multiple Background Knowledge in Location-Based Social Networks

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 262 ◽  
Author(s):  
Xiao Pan ◽  
Weizhang Chen ◽  
Lei Wu
Keyword(s):  
Social Networks ◽  
Location Privacy ◽  
Background Knowledge ◽  
Mobile User ◽  
Inference Model ◽  
Point Of Interest ◽  
Information User ◽  
Location Inference ◽  
Location Based Social Networks ◽  
Location Privacy Protection

Location-based social networks have been widely used. However, due to the lack of effective and safe data management, a large number of privacy disclosures commonly occur. Thus, academia and industry have needed to focus more on location privacy protection. This paper proposes a novel location attack method using multiple background options to infer the hidden locations of mobile users. In order to estimate the possibility of a hidden position being visited by a user, two hidden location attack models are proposed, i.e., a Bayesian hidden location inference model and the multi-factor fusion based hidden location inference model. Multiple background factors, including the check-in sequences, temporal information, user social networks, personalized service preferences, point of interest (POI) popularities, etc., are considered in the two models. Moreover, a hidden location inference algorithm is provided as well. Finally, a series of experiments are conducted on two real check-in data examples to evaluate the accuracy of the model and verify the validity of the proposed algorithm. The experimental results show that multiple background knowledge fusion provides benefits for improving location inference precision.

scholarly journals HiCNN2: Enhancing the Resolution of Hi-C Data Using an Ensemble of Convolutional Neural Networks

Genes ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 862
Author(s):  
Tong Liu ◽  
Zheng Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Mean Squared Error ◽  
Correlation Coefficients ◽  
Chromatin Interactions ◽  
Squared Error ◽  
Genome Wide

We present a deep-learning package named HiCNN2 to learn the mapping between low-resolution and high-resolution Hi-C (a technique for capturing genome-wide chromatin interactions) data, which can enhance the resolution of Hi-C interaction matrices. The HiCNN2 package includes three methods each with a different deep learning architecture: HiCNN2-1 is based on one single convolutional neural network (ConvNet); HiCNN2-2 consists of an ensemble of two different ConvNets; and HiCNN2-3 is an ensemble of three different ConvNets. Our evaluation results indicate that HiCNN2-enhanced high-resolution Hi-C data achieve smaller mean squared error and higher Pearson’s correlation coefficients with experimental high-resolution Hi-C data compared with existing methods HiCPlus and HiCNN. Moreover, all of the three HiCNN2 methods can recover more significant interactions detected by Fit-Hi-C compared to HiCPlus and HiCNN. Based on our evaluation results, we would recommend using HiCNN2-1 and HiCNN2-3 if recovering more significant interactions from Hi-C data is of interest, and HiCNN2-2 and HiCNN if the goal is to achieve higher reproducibility scores between the enhanced Hi-C matrix and the real high-resolution Hi-C matrix.

scholarly journals Finding Potential Propagators and Customers in Location-Based Social Networks: An Embedding-Based Approach

2020 ◽  
Vol 10 (22) ◽  
pp. 8003
Author(s):  
Yi-Chun Chen ◽  
Cheng-Te Li
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Main Idea ◽  
Feature Representation ◽  
Specific Point ◽  
Point Of Interest ◽  
The Future ◽  
Location Based Social Networks ◽  
Low Dimensional ◽  
Embedding Methods

In the scenarios of location-based social networks (LBSN), the goal of location promotion is to find information propagators to promote a specific point-of-interest (POI). While existing studies mainly focus on accurately recommending POIs for users, less effort is made for identifying propagators in LBSN. In this work, we propose and tackle two novel tasks, Targeted Propagator Discovery (TPD) and Targeted Customer Discovery (TCD), in the context of Location Promotion. Given a target POI l to be promoted, TPD aims at finding a set of influential users, who can generate more users to visit l in the future, and TCD is to find a set of potential users, who will visit l in the future. To deal with TPD and TCD, we propose a novel graph embedding method, LBSN2vec. The main idea is to jointly learn a low dimensional feature representation for each user and each location in an LBSN. Equipped with learned embedding vectors, we propose two similarity-based measures, Influential and Visiting scores, to find potential targeted propagators and customers. Experiments conducted on a large-scale Instagram LBSN dataset exhibit that LBSN2vec and its variant can significantly outperform well-known network embedding methods in both tasks.

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