scholarly journals Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation

2019 ◽  
Vol 33 ◽  
pp. 5877-5884 ◽  
Author(s):  
Pengpeng Zhao ◽  
Haifeng Zhu ◽  
Yanchi Liu ◽  
Jiajie Xu ◽  
Zhixu Li ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Service Providers ◽  
The State ◽  
Short Term ◽  
Temporal Relationships ◽  
Poi Recommendation ◽  
Temporal Intervals ◽  
Spatio Temporal ◽  
Real World Datasets ◽  
Location Based Social Networks

Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users. However, the state-of-the-art Recurrent Neural Networks (RNNs) rarely consider the spatio-temporal intervals between neighbor check-ins, which are essential for modeling user check-in behaviors in next POI recommendation. To this end, in this paper, we propose a new Spatio-Temporal Gated Network (STGN) by enhancing long-short term memory network, where spatio-temporal gates are introduced to capture the spatio-temporal relationships between successive checkins. Specifically, two pairs of time gate and distance gate are designed to control the short-term interest and the longterm interest updates, respectively. Moreover, we introduce coupled input and forget gates to reduce the number of parameters and further improve efficiency. Finally, we evaluate the proposed model using four real-world datasets from various location-based social networks. The experimental results show that our model significantly outperforms the state-ofthe-art approaches for next POI 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 Learning Robot Speech Models to Predict Speech Acts in HRI

2016 ◽  
Vol 9 (1) ◽  
pp. 295-306
Author(s):  
Ankuj Arora ◽  
Humbert Fiorino ◽  
Damien Pellier ◽  
Sylvie Pesty
Keyword(s):  
Speech Acts ◽  
Short Term Memory ◽  
The State ◽  
Short Term ◽  
Long Run ◽  
Action Sequences ◽  
Long Short Term Memory ◽  
Max Sat ◽  
Speech Model

Abstract In order to be acceptable and able to “camouflage” into their physio-social context in the long run, robots need to be not just functional, but autonomously psycho-affective as well. This motivates a long term necessity of introducing behavioral autonomy in robots, so they can autonomously communicate with humans without the need of “wizard” intervention. This paper proposes a technique to learn robot speech models from human-robot dialog exchanges. It views the entire exchange in the Automated Planning (AP) paradigm, representing the dialog sequences (speech acts) in the form of action sequences that modify the state of the world upon execution, gradually propelling the state to a desired goal. We then exploit intra-action and inter-action dependencies, encoding them in the form of constraints. We attempt to satisfy these constraints using aweighted maximum satisfiability model known as MAX-SAT, and convert the solution into a speech model. This model could have many uses, such as planning of fresh dialogs. In this study, the learnt model is used to predict speech acts in the dialog sequences using the sequence labeling (predicting future acts based on previously seen ones) capabilities of the LSTM (Long Short Term Memory) class of recurrent neural networks. Encouraging empirical results demonstrate the utility of this learnt model and its long term potential to facilitate autonomous behavioral planning of robots, an aspect to be explored in future works.

scholarly journals ON MEMORY AND STRUCTURAL DYNAMISM IN EXCITABLE CELLULAR AUTOMATA WITH DEFENSIVE INHIBITION

2008 ◽  
Vol 18 (02) ◽  
pp. 527-539 ◽  
Author(s):  
RAMÓN ALONSO-SANZ ◽  
ANDREW ADAMATZKY
Keyword(s):  
Cellular Automata ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Short Term ◽  
Resting Cell ◽  
Fixed Topology ◽  
Dynamical Topology ◽  
Novel Complex ◽  
Spatio Temporal ◽  
Update Rules

Commonly studied cellular automata are memoryless and have fixed topology of connections between cells. However by allowing updates of links and short-term memory in cells we may potentially discover novel complex regimes of spatio-temporal dynamics. Moreover, by adding memory and dynamical topology to state update rules we somehow forge elementary but nontraditional models of neurons networks (aka neuron layers in frontal parts). In the present paper, we demonstrate how this can be done on a self-inhibitory excitable cellular automata. These automata imitate a phenomenon of inhibition caused by hight-strength stimulus: a resting cell excites if there are one or two excited neighbors, the cell remains resting otherwise. We modify the automaton by allowing cells to have few-steps memories, and create links between neighboring cells removed or generated depending on the states of the cells.

scholarly journals Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM

2019 ◽  
Vol 9 (20) ◽  
pp. 4237 ◽  
Author(s):  
Tuong Le ◽  
Minh Thanh Vo ◽  
Bay Vo ◽  
Eenjun Hwang ◽  
Seungmin Rho ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Performance Metrics ◽  
Short Term Memory ◽  
State Of The Art ◽  
Electric Energy ◽  
The State ◽  
Short Term ◽  
Electric Energy Consumption ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

The electric energy consumption prediction (EECP) is an essential and complex task in intelligent power management system. EECP plays a significant role in drawing up a national energy development policy. Therefore, this study proposes an Electric Energy Consumption Prediction model utilizing the combination of Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) that is named EECP-CBL model to predict electric energy consumption. In this framework, two CNNs in the first module extract the important information from several variables in the individual household electric power consumption (IHEPC) dataset. Then, Bi-LSTM module with two Bi-LSTM layers uses the above information as well as the trends of time series in two directions including the forward and backward states to make predictions. The obtained values in the Bi-LSTM module will be passed to the last module that consists of two fully connected layers for finally predicting the electric energy consumption in the future. The experiments were conducted to compare the prediction performances of the proposed model and the state-of-the-art models for the IHEPC dataset with several variants. The experimental results indicate that EECP-CBL framework outperforms the state-of-the-art approaches in terms of several performance metrics for electric energy consumption prediction on several variations of IHEPC dataset in real-time, short-term, medium-term and long-term timespans.

scholarly journals Cubic LSTMs for Video Prediction

2019 ◽  
Vol 33 ◽  
pp. 8263-8270 ◽  
Author(s):  
Hehe Fan ◽  
Linchao Zhu ◽  
Yi Yang
Keyword(s):  
Learning Communities ◽  
Moving Objects ◽  
Short Term Memory ◽  
Robot Learning ◽  
Motion Prediction ◽  
Short Term ◽  
The Core ◽  
Long Short Term Memory ◽  
Real World Datasets ◽  
Video Prediction

Predicting future frames in videos has become a promising direction of research for both computer vision and robot learning communities. The core of this problem involves moving object capture and future motion prediction. While object capture specifies which objects are moving in videos, motion prediction describes their future dynamics. Motivated by this analysis, we propose a Cubic Long Short-Term Memory (CubicLSTM) unit for video prediction. CubicLSTM consists of three branches, i.e., a spatial branch for capturing moving objects, a temporal branch for processing motions, and an output branch for combining the first two branches to generate predicted frames. Stacking multiple CubicLSTM units along the spatial branch and output branch, and then evolving along the temporal branch can form a cubic recurrent neural network (CubicRNN). Experiment shows that CubicRNN produces more accurate video predictions than prior methods on both synthetic and real-world datasets.

scholarly journals Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation

2020 ◽  
Vol 34 (01) ◽  
pp. 214-221 ◽  
Author(s):  
Ke Sun ◽  
Tieyun Qian ◽  
Tong Chen ◽  
Yile Liang ◽  
Quoc Viet Hung Nguyen ◽  
...  
Keyword(s):  
State Of The Art ◽  
User Preferences ◽  
Short Term ◽  
Preference Modeling ◽  
Point Of Interest ◽  
Proposed Model ◽  
Poi Recommendation ◽  
Novel Method ◽  
Real World Datasets

Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task. However, existing RNN-based methods either neglect users' long-term preferences or overlook the geographical relations among recently visited POIs when modeling users' short-term preferences, thus making the recommendation results unreliable. To address the above limitations, we propose a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation. In particular, the proposed model consists of a nonlocal network for long-term preference modeling and a geo-dilated RNN for short-term preference learning. Extensive experiments on two real-world datasets demonstrate that our model yields significant improvements over the state-of-the-art methods.

scholarly journals A Novel LSTM for Multivariate Time Series with Massive Missingness

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2832
Author(s):  
Nazanin Fouladgar ◽  
Kary Främling
Keyword(s):  
Time Series ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Multivariate Time Series ◽  
Streaming Data ◽  
Short Term ◽  
Time Intervals ◽  
Long Short Term Memory ◽  
Real World Datasets ◽  
Novel Model

Multivariate time series with missing data is ubiquitous when the streaming data is collected by sensors or any other recording instruments. For instance, the outdoor sensors gathering different meteorological variables may encounter low material sensitivity to specific situations, leading to incomplete information gathering. This is problematic in time series prediction with massive missingness and different missing rate of variables. Contribution addressing this problem on the regression task of meteorological datasets by employing Long Short-Term Memory (LSTM), capable of controlling the information flow with its memory unit, is still missing. In this paper, we propose a novel model called forward and backward variable-sensitive LSTM (FBVS-LSTM) consisting of two decay mechanisms and some informative data. The model inputs are mainly the missing indicator, time intervals of missingness in both forward and backward direction and missing rate of each variable. We employ this information to address the so-called missing not at random (MNAR) mechanism. Separately learning the features of each parameter, the model becomes adapted to deal with massive missingness. We conduct our experiment on three real-world datasets for the air pollution forecasting. The results demonstrate that our model performed well along with other LSTM-derivation models in terms of prediction accuracy.

scholarly journals Short Term Electric Load Forecasting Based on Data Transformation and Statistical Machine Learning

2020 ◽  
Vol 11 (1) ◽  
pp. 158
Author(s):  
Nikos Andriopoulos ◽  
Aristeidis Magklaras ◽  
Alexios Birbas ◽  
Alex Papalexopoulos ◽  
Christos Valouxis ◽  
...  
Keyword(s):  
Distribution System ◽  
Short Term Memory ◽  
State Of The Art ◽  
Load Forecasting ◽  
The State ◽  
Neural Nets ◽  
Interactive System ◽  
Electric Load ◽  
Short Term ◽  
Electric Load Forecasting

The continuous penetration of renewable energy resources (RES) into the energy mix and the transition of the traditional electric grid towards a more intelligent, flexible and interactive system, has brought electrical load forecasting to the foreground of smart grid planning and operation. Predicting the electric load is a challenging task due to its high volatility and uncertainty, either when it refers to the distribution system or to a single household. In this paper, a novel methodology is introduced which leverages the advantages of the state-of-the-art deep learning algorithms and specifically the Convolution Neural Nets (CNN). The main feature of the proposed methodology is the exploitation of the statistical properties of each time series dataset, so as to optimize the hyper-parameters of the neural network and in addition transform the given dataset into a form that allows maximum exploitation of the CNN algorithm’s advantages. The proposed algorithm is compared with the LSTM (Long Short Term Memory) technique which is the state of the art solution for electric load forecasting. The evaluation of the algorithms was conducted by employing three open-source, publicly available datasets. The experimental results show strong evidence of the effectiveness of the proposed methodology.

scholarly journals Predicting of air pollutant concentrations based on spatio-temporal attention convolutional LSTM networks

2021 ◽  
Author(s):  
P. Jiang ◽  
I. Bychkov ◽  
J. Liu ◽  
A. Hmelnov
Keyword(s):  
Neural Networks ◽  
Short Term Memory ◽  
Air Pollutant ◽  
Pollutant Concentration ◽  
Support Vector ◽  
Temporal Attention ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Spatio Temporal

Forecasting of air pollutant concentration, which is influenced by air pollution accumulation, traffic flow and industrial emissions, has attracted extensive attention for decades. In this paper, we propose a spatio-temporal attention convolutional long short term memory neural networks (Attention-CNN-LSTM) for air pollutant concentration forecasting. Firstly, we analyze the Granger causalities between different stations and establish a hyperparametric Gaussian vector weight function to determine spatial autocorrelation variables, which is used as part of the input feature. Secondly, convolutional neural networks (CNN) is employed to extract the temporal dependence and spatial correlation of the input, while feature maps and channels are weighted by attention mechanism, so as to improve the effectiveness of the features. Finally, a depth long short term memory (LSTM) based time series predictor is established for learning the long-term and short-term dependence of pollutant concentration. In order to reduce the effect of diverse complex factors on LSTM, inherent features are extracted from historical air pollutant concentration data meteorological data and timestamp information are incorporated into the proposed model. Extensive experiments were performed using the Attention-CNNLSTM, autoregressive integrated moving average (ARIMA), support vector regression (SVR), traditional LSTM and CNN, respectively. The results demonstrated that the feasibility and practicability of Attention-CNN-LSTM on estimating CO and NO concentration.

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