scholarly journals A Hybrid CNN-Based Review Helpfulness Filtering Model for Improving E-Commerce Recommendation Service

2021 ◽  
Vol 11 (18) ◽  
pp. 8613
Author(s):  
Qinglong Li ◽  
Xinzhe Li ◽  
Byunghyun Lee ◽  
Jaekyeong Kim
Keyword(s):  
Neural Network ◽  
Recommender System ◽  
User Satisfaction ◽  
Short Term Memory ◽  
Positive Impact ◽  
Classification Model ◽  
Personalized Recommendation ◽  
Purchasing Decisions ◽  
Review Helpfulness ◽  
Information Exploration

As the e-commerce market grows worldwide, personalized recommendation services have become essential to users’ personalized items or services. They can decrease the cost of user information exploration and have a positive impact on corporate sales growth. Recently, many studies have been actively conducted using reviews written by users to address traditional recommender system research problems. However, reviews can include content that is not conducive to purchasing decisions, such as advertising, false reviews, or fake reviews. Using such reviews to provide recommendation services can lower the recommendation performance as well as a trust in the company. This study proposes a novel review of the helpfulness-based recommendation methodology (RHRM) framework to support users’ purchasing decisions in personalized recommendation services. The core of our framework is a review semantics extractor and a user/item recommendation generator. The review semantics extractor learns reviews representations in a convolutional neural network and bidirectional long short-term memory hybrid neural network for review helpfulness classification. The user/item recommendation generator models the user’s preference on items based on their past interactions. Here, past interactions indicate only records in which the user-written reviews of items are helpful. Since many reviews do not have helpfulness scores, we first propose a helpfulness classification model to reflect the review helpfulness that significantly impacts users’ purchasing decisions in personalized recommendation services. The helpfulness classification model is trained about limited reviews utilizing helpfulness scores. Several experiments with the Amazon dataset show that if review helpfulness information is used in the recommender system, performance such as the accuracy of personalized recommendation service can be further improved, thereby enhancing user satisfaction and further increasing trust in the company.

scholarly journals Chinese Text Classification Model Based on Deep Learning

2018 ◽  
Vol 10 (11) ◽  
pp. 113 ◽  
Author(s):  
Yue Li ◽  
Xutao Wang ◽  
Pengjian Xu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Language Processing ◽  
Chinese Text ◽  
Text Classification ◽  
Short Term Memory ◽  
Classification Model ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Text classification is of importance in natural language processing, as the massive text information containing huge amounts of value needs to be classified into different categories for further use. In order to better classify text, our paper tries to build a deep learning model which achieves better classification results in Chinese text than those of other researchers’ models. After comparing different methods, long short-term memory (LSTM) and convolutional neural network (CNN) methods were selected as deep learning methods to classify Chinese text. LSTM is a special kind of recurrent neural network (RNN), which is capable of processing serialized information through its recurrent structure. By contrast, CNN has shown its ability to extract features from visual imagery. Therefore, two layers of LSTM and one layer of CNN were integrated to our new model: the BLSTM-C model (BLSTM stands for bi-directional long short-term memory while C stands for CNN.) LSTM was responsible for obtaining a sequence output based on past and future contexts, which was then input to the convolutional layer for extracting features. In our experiments, the proposed BLSTM-C model was evaluated in several ways. In the results, the model exhibited remarkable performance in text classification, especially in Chinese texts.

scholarly journals Identifying Foreign Tourists’ Nationality from Mobility Traces via LSTM Neural Network and Location Embeddings

2019 ◽  
Vol 9 (14) ◽  
pp. 2861 ◽  
Author(s):  
Alessandro Crivellari ◽  
Euro Beinat
Keyword(s):  
Neural Network ◽  
Motion Tracking ◽  
Large Scale ◽  
Short Term Memory ◽  
Human Mobility ◽  
User Profiling ◽  
Personalized Recommendation ◽  
Mobility Patterns ◽  
Trajectory Data ◽  
Short Term

The interest in human mobility analysis has increased with the rapid growth of positioning technology and motion tracking, leading to a variety of studies based on trajectory recordings. Mapping the routes that people commonly perform was revealed to be very useful for location-based service applications, where individual mobility behaviors can potentially disclose meaningful information about each customer and be fruitfully used for personalized recommendation systems. This paper tackles a novel trajectory labeling problem related to the context of user profiling in “smart” tourism, inferring the nationality of individual users on the basis of their motion trajectories. In particular, we use large-scale motion traces of short-term foreign visitors as a way of detecting the nationality of individuals. This task is not trivial, relying on the hypothesis that foreign tourists of different nationalities may not only visit different locations, but also move in a different way between the same locations. The problem is defined as a multinomial classification with a few tens of classes (nationalities) and sparse location-based trajectory data. We hereby propose a machine learning-based methodology, consisting of a long short-term memory (LSTM) neural network trained on vector representations of locations, in order to capture the underlying semantics of user mobility patterns. Experiments conducted on a real-world big dataset demonstrate that our method achieves considerably higher performances than baseline and traditional approaches.

scholarly journals A Bi–LSTM and k-NN Based Method for Detecting Major Time Zones of Overloaded Vehicles

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1160
Author(s):  
Sangmin Park ◽  
Byung-Won On ◽  
Ryong Lee ◽  
Min-Woo Park ◽  
Sang-Hwan Lee
Keyword(s):  
Neural Network ◽  
Urban Areas ◽  
Traffic Accidents ◽  
Time Series Data ◽  
Short Term Memory ◽  
New Method ◽  
Classification Model ◽  
Series Data ◽  
Traffic Condition ◽  
Time Zones

Overloaded vehicles such as large cargo trucks tend to cause large traffic accidents. Such traffic accidents often bring high mortality rates, including injuries and deaths, and cause fatal damage to road structures such as roads and bridges. Therefore, there is a vicious circle in which a lot of the budgets is spent for accident restoration and road maintenance. It is important to control overloaded vehicles that are around roads in urban areas. However, it often takes a lot of manpower to track down on overloaded vehicles at appropriate interception points during a specific time. Moreover, the drivers tend to avoid interception by bypassing the interception point, while exchanging interception information with each other. In this work, the main bridges in a city are chosen as the interception point. Since installing vehicle-weighing devices on the road surface is expensive and the devices cause frequent faults after the installation, inexpensive general-purpose Internet of Things (IoT) sensors, such as acceleration and gyroscope sensors, are installed on the bridges. First, assuming that the sensing value of the overloaded vehicle is different from the nonoverloaded vehicle, we investigate the difference in the sensed values between the overloaded and nonoverloaded vehicles. Then, based on the hypothesis, we propose a new method to identify prime time zones with overloaded vehicles. Technically, the proposed method comprises two steps. In the first step, we propose a new bridge traffic classification model using Bidirectional Long Short-Term Memory (Bi–LSTM) that automatically classifies time series data to either high or low traffic condition. The Bi–LSTM model has higher accuracy than existing neural network models because it has a symmetric neural network structure, by which input information can be processed in forward and backward directions. In the second step, we propose a new method of automatically identifying top-k time zones with many overloaded vehicles under the high traffic condition. It first uses the k-Nearest Neighbor (NN) algorithm to find the sensing value, most similar to the actual sensing value of the overloaded vehicle, in the high traffic cluster. According to the experimental results, there is a high difference of the sensing values between the overloaded and the nonoverloaded vehicle, through statistical verification. Also, the accuracy of the proposed method in the first step is ~75%, and the top-k time zones in which overloaded vehicles are crowded are identified automatically.

scholarly journals Hierarchical Spatiotemporal Electroencephalogram Feature Learning and Emotion Recognition With Attention-Based Antagonism Neural Network

2021 ◽  
Vol 15 ◽  
Author(s):  
Pengwei Zhang ◽  
Chongdan Min ◽  
Kangjia Zhang ◽  
Wen Xue ◽  
Jingxia Chen
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Short Term Memory ◽  
Brain Area ◽  
Brain Regions ◽  
Dynamic Responses ◽  
Classification Model ◽  
Emotion Classification ◽  
Global Brain ◽  
Eeg Features

Inspired by the neuroscience research results that the human brain can produce dynamic responses to different emotions, a new electroencephalogram (EEG)-based human emotion classification model was proposed, named R2G-ST-BiLSTM, which uses a hierarchical neural network model to learn more discriminative spatiotemporal EEG features from local to global brain regions. First, the bidirectional long- and short-term memory (BiLSTM) network is used to obtain the internal spatial relationship of EEG signals on different channels within and between regions of the brain. Considering the different effects of various cerebral regions on emotions, the regional attention mechanism is introduced in the R2G-ST-BiLSTM model to determine the weight of different brain regions, which could enhance or weaken the contribution of each brain area to emotion recognition. Then a hierarchical BiLSTM network is again used to learn the spatiotemporal EEG features from regional to global brain areas, which are then input into an emotion classifier. Especially, we introduce a domain discriminator to work together with the classifier to reduce the domain offset between the training and testing data. Finally, we make experiments on the EEG data of the DEAP and SEED datasets to test and compare the performance of the models. It is proven that our method achieves higher accuracy than those of the state-of-the-art methods. Our method provides a good way to develop affective brain–computer interface applications.

scholarly journals A Cold Start Context-Aware Recommender System for Tour Planning Using Artificial Neural Network and Case Based Reasoning

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Zahra Bahramian ◽  
Rahim Ali Abbaspour ◽  
Christophe Claramunt
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Recommender System ◽  
User Satisfaction ◽  
Cold Start ◽  
Case Based Reasoning ◽  
Specific Information ◽  
Context Aware ◽  
Artificial Neural ◽  
Case Based

Nowadays, large amounts of tourism information and services are available over the Web. This makes it difficult for the user to search for some specific information such as selecting a tour in a given city as an ordered set of points of interest. Moreover, the user rarely knows all his needs upfront and his preferences may change during a recommendation process. The user may also have a limited number of initial ratings and most often the recommender system is likely to face the well-known cold start problem. The objective of the research presented in this paper is to introduce a hybrid interactive context-aware tourism recommender system that takes into account user’s feedbacks and additional contextual information. It offers personalized tours to the user based on his preferences thanks to the combination of a case based reasoning framework and an artificial neural network. The proposed method has been tried in the city of Tehran in Iran. The results show that the proposed method outperforms current artificial neural network methods and combinations of case based reasoning withk-nearest neighbor methods in terms of user effort, accuracy, and user satisfaction.

scholarly journals Automated Detection of Multiple Lesions on Chest X-ray Images: Classification Using a Neural Network Technique with Association-Specific Contexts

2020 ◽  
Vol 10 (5) ◽  
pp. 1742 ◽  
Author(s):  
Shuaijing Xu ◽  
Junqi Guo ◽  
Guangzhi Zhang ◽  
Rongfang Bie
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Lung Cancer Mortality ◽  
Automated Detection ◽  
Classification Model ◽  
Diagnostic Efficiency ◽  
X Ray ◽  
Multiple Lesions ◽  
Model Combining ◽  
Chest X Ray

Automated detection of lung lesions on Chest X-ray images shows good performance to reduce lung cancer mortality. However, it is difficult to detect multiple lesions of single image well and truly, and additional efforts are needed to improve diagnostic efficiency and quality. In this paper, a multi-label classification model combining attention-based neural networks and association-specific contexts is proposed for the detection of multiple lesions on chest X-ray images. A convolutional neural network and a long short-term memory network are first aligned by an attention mechanism to take advantage of both image and text information for the detection, called CNN-ATTENTION-LSTM (CAL) network. In addition, a mining method of implicit association strength to obtain an association network of chest lesions (CLA) network is designed to guide the training of CAL network. The CLA network provides possible clinical relationships between lesions to help the CAL network obtain better predictions. Experimental results on ChestX-ray14 dataset show that our method outperforms some state-of-the-art models under the metrics of area under curve (AUC), precision, recall, and F-score and achieves up to 85.4% in the case of atelectasis and infiltration. It indicates that the method may be useful in the computer-aided detection of multiple lesions on chest X-ray images.

scholarly journals Pre-trained Deep Convolution Neural Network Model With Attention for Speech Emotion Recognition

2021 ◽  
Vol 12 ◽  
Author(s):  
Hua Zhang ◽  
Ruoyun Gou ◽  
Jili Shang ◽  
Fangyao Shen ◽  
Yifan Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Short Term Memory ◽  
Convolution Neural Network ◽  
Classification Model ◽  
Speech Emotion Recognition ◽  
Deep Convolution Neural Network ◽  
Long Short Term Memory ◽  
High Level ◽  
Better Than

Speech emotion recognition (SER) is a difficult and challenging task because of the affective variances between different speakers. The performances of SER are extremely reliant on the extracted features from speech signals. To establish an effective features extracting and classification model is still a challenging task. In this paper, we propose a new method for SER based on Deep Convolution Neural Network (DCNN) and Bidirectional Long Short-Term Memory with Attention (BLSTMwA) model (DCNN-BLSTMwA). We first preprocess the speech samples by data enhancement and datasets balancing. Secondly, we extract three-channel of log Mel-spectrograms (static, delta, and delta-delta) as DCNN input. Then the DCNN model pre-trained on ImageNet dataset is applied to generate the segment-level features. We stack these features of a sentence into utterance-level features. Next, we adopt BLSTM to learn the high-level emotional features for temporal summarization, followed by an attention layer which can focus on emotionally relevant features. Finally, the learned high-level emotional features are fed into the Deep Neural Network (DNN) to predict the final emotion. Experiments on EMO-DB and IEMOCAP database obtain the unweighted average recall (UAR) of 87.86 and 68.50%, respectively, which are better than most popular SER methods and demonstrate the effectiveness of our propose method.

scholarly journals Classification of Biomedical Texts for Cardiovascular Diseases with Deep Neural Network Using a Weighted Feature Representation Method

Healthcare ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 392
Author(s):  
Nizar Ahmed ◽  
Fatih Dilmaç ◽  
Adil Alpkocak
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Cardiovascular Diseases ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Multiclass Classification ◽  
Feature Representation ◽  
Classification Model ◽  
Biomedical Texts

This study aims to improve the performance of multiclass classification of biomedical texts for cardiovascular diseases by combining two different feature representation methods, i.e., bag-of-words (BoW) and word embeddings (WE). To hybridize the two feature representations, we investigated a set of possible statistical weighting schemes to combine with each element of WE vectors, which were term frequency (TF), inverse document frequency (IDF) and class probability (CP) methods. Thus, we built a multiclass classification model using a bidirectional long short-term memory (BLSTM) with deep neural networks for all investigated operations of feature vector combinations. We used MIMIC III and the PubMed dataset for the developing language model. To evaluate the performance of our weighted feature representation approaches, we conducted a set of experiments for examining multiclass classification performance with the deep neural network model and other state-of-the-art machine learning (ML) approaches. In all experiments, we used the OHSUMED-400 dataset, which includes PubMed abstracts related with specifically one class over 23 cardiovascular disease categories. Afterwards, we presented the results obtained from experiments and provided a comparison with related research in the literature. The results of the experiment showed that our BLSTM model with the weighting techniques outperformed the baseline and other machine learning approaches in terms of validation accuracy. Finally, our model outperformed the scores of related studies in the literature. This study shows that weighted feature representation improves the performance of the multiclass classification.

scholarly journals Deep semi-supervised learning with weight map for review helpfulness prediction

2021 ◽  
pp. 44-44
Author(s):  
Hua Yin ◽  
Zhensheng Hu ◽  
Yahui Peng ◽  
Zhijian Wang ◽  
Guanglong Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Supervised Learning ◽  
Mapping Method ◽  
Product Reviews ◽  
Purchasing Decisions ◽  
The Past ◽  
Review Helpfulness ◽  
Series Of Experiments ◽  
Novel Model ◽  
Weight Map

Helpful online product reviews, which includemassive information, have large impacts on customers? purchasing decisions. In most of e-commerce plat forms, the helpfulness of reviews are decided by the votes from other customers. Making full use of these reviews with votes has enormous commercial value, especially in product recommendation. It drives researchers to study the technologies about how to evaluate the review helpfulness automatically. Although Deep Neural Network(DNN), learning from the historical reviews and labels, computed by the votes, has demonstrated effective results, it still has suffered insufficient labeled reviews problem. When the helpfulness of a large number of reviews is unknown for lack of votes, or some useful latest reviews with less votes are submerged by the past reviews, the accuracy of current DNN model decreases quickly. Therefore, we propose an end-to-end deep semi-supervised learning model with weight map, which makes full use of the unlabeled reviews. The training process in this model is divided into three stages:obtaining base classifier by less labeled reviews, iteratively applying weight map strategy on large unlabeled reviews to obtain pseudo-labeled reviews, training on above combined reviews to obtain the re-training classifier. Based on this novel model, we develop an algorithm and conduct a series of experiments, on Amazon Review Dataset, from the aspects of the baseline neural network selection and the strategies comparisons, including two labeling and three weighting strategies. The experimental results demonstrate the effectiveness of our method on utilizing the unlabeled data. And our findings show that the model adopted batch labeling strategy and non-linear weight mapping method has achieved the best performance.

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