Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data

The collection of physiological data from people has been facilitated due to the mass use of cheap wearable devices. Although the accuracy is low compared to specialized healthcare devices, these can be widely applied in other contexts. This study proposes the architecture for a tourist experiences recommender system (TERS) based on the user’s emotional states who wear these devices. The issue lies in detecting emotion from Heart Rate (HR) measurements obtained from these wearables. Unlike most state-of-the-art studies, which have elicited emotions in controlled experiments and with high-accuracy sensors, this research’s challenge consisted of emotion recognition (ER) in the daily life context of users based on the gathering of HR data. Furthermore, an objective was to generate the tourist recommendation considering the emotional state of the device wearer. The method used comprises three main phases: The first was the collection of HR measurements and labeling emotions through mobile applications. The second was emotional detection using deep learning algorithms. The final phase was the design and validation of the TERS-ER. In this way, a dataset of HR measurements labeled with emotions was obtained as results. Among the different algorithms tested for ER, the hybrid model of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks had promising results. Moreover, concerning TERS, Collaborative Filtering (CF) using CNN showed better performance.

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Recommender System based on Deep Neural Network and Long Short Term Memory

10.1109/contesa52813.2021.9657131 ◽

2021 ◽

Author(s):

Sandeep Kumar Rachamadugu ◽

Jayanarayana Reddy Dwaram ◽

Kiran Rao Patike

Keyword(s):

Neural Network ◽

Recommender System ◽

Deep Neural Network ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Dance Emotion Recognition Based on Laban Motion Analysis Using Convolutional Neural Network and Long Short-Term Memory

IEEE Access ◽

10.1109/access.2020.3007956 ◽

2020 ◽

Vol 8 ◽

pp. 124928-124938 ◽

Cited By ~ 1

Author(s):

Simin Wang ◽

Junhuai Li ◽

Ting Cao ◽

Huaijun Wang ◽

Pengjia Tu ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Emotion Recognition ◽

Motion Analysis ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Audio-Textual Emotion Recognition Based on Improved Neural Networks

Mathematical Problems in Engineering ◽

10.1155/2019/2593036 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Linqin Cai ◽

Yaxin Hu ◽

Jiangong Dong ◽

Sitong Zhou

Keyword(s):

Neural Network ◽

Emotion Recognition ◽

Short Term Memory ◽

Recognition Accuracy ◽

Recognition System ◽

Speech Emotion Recognition ◽

Short Term ◽

Term Memory ◽

Emotional Recognition ◽

Long Short Term Memory

With the rapid development in social media, single-modal emotion recognition is hard to satisfy the demands of the current emotional recognition system. Aiming to optimize the performance of the emotional recognition system, a multimodal emotion recognition model from speech and text was proposed in this paper. Considering the complementarity between different modes, CNN (convolutional neural network) and LSTM (long short-term memory) were combined in a form of binary channels to learn acoustic emotion features; meanwhile, an effective Bi-LSTM (bidirectional long short-term memory) network was resorted to capture the textual features. Furthermore, we applied a deep neural network to learn and classify the fusion features. The final emotional state was determined by the output of both speech and text emotion analysis. Finally, the multimodal fusion experiments were carried out to validate the proposed model on the IEMOCAP database. In comparison with the single modal, the overall recognition accuracy of text increased 6.70%, and that of speech emotion recognition soared 13.85%. Experimental results show that the recognition accuracy of our multimodal is higher than that of the single modal and outperforms other published multimodal models on the test datasets.

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Long Short-Term Memory Based Movie Recommendation

Science & Technology Development Journal - Engineering and Technology ◽

10.32508/stdjet.v3isi1.540 ◽

2020 ◽

Vol 3 (SI1) ◽

pp. SI1-SI9

Author(s):

Sang Thi Thanh Nguyen ◽

Bao Duy Tran

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Recommender System ◽

Business Models ◽

Short Term Memory ◽

User Behavior ◽

Learning Method ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Recommender systems (RS) have become a fundamental tool for helping users make decisions around millions of different choices nowadays – the era of Big Data. It brings a huge benefit for many business models around the world due to their effectiveness on the target customers. A lot of recommendation models and techniques have been proposed and many accomplished incredible outcomes. Collaborative filtering and content-based filtering methods are common, but these both have some disadvantages. A critical one is that they only focus on a user's long-term static preference while ignoring his or her short-term transactional patterns, which results in missing the user's preference shift through the time. In this case, the user's intent at a certain time point may be easily submerged by his or her historical decision behaviors, which leads to unreliable recommendations. To deal with this issue, a session of user interactions with the items can be considered as a solution. In this study, Long Short-Term Memory (LSTM) networks will be analyzed to be applied to user sessions in a recommender system. The MovieLens dataset is considered as a case study of movie recommender systems. This dataset is preprocessed to extract user-movie sessions for user behavior discovery and making movie recommendations to users. Several experiments have been carried out to evaluate the LSTM-based movie recommender system. In the experiments, the LSTM networks are compared with a similar deep learning method, which is Recurrent Neural Networks (RNN), and a baseline machine learning method, which is the collaborative filtering using item-based nearest neighbors (item-KNN). It has been found that the LSTM networks are able to be improved by optimizing their hyperparameters and outperform the other methods when predicting the next movies interested by users.

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Long Short Term Memory Hyperparameter Optimization for a Neural Network Based Emotion Recognition Framework

IEEE Access ◽

10.1109/access.2018.2868361 ◽

2018 ◽

Vol 6 ◽

pp. 49325-49338 ◽

Cited By ~ 20

Author(s):

Bahareh Nakisa ◽

Mohammad Naim Rastgoo ◽

Andry Rakotonirainy ◽

Frederic Maire ◽

Vinod Chandran

Keyword(s):

Neural Network ◽

Emotion Recognition ◽

Short Term Memory ◽

Short Term ◽

Hyperparameter Optimization ◽

Term Memory ◽

Long Short Term Memory

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Emotion Recognition based on Fusion of Long Short-Term Memory Networks and SVMs

Digital Signal Processing ◽

10.1016/j.dsp.2021.103153 ◽

2021 ◽

pp. 103153

Author(s):

Tian Chen ◽

Hongfang Yin ◽

Xiaohui Yuan ◽

Yu Gu ◽

Fuji Ren ◽

...

Keyword(s):

Emotion Recognition ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Attention-LSTM-Attention Model for Speech Emotion Recognition and Analysis of IEMOCAP Database

Electronics ◽

10.3390/electronics9050713 ◽

2020 ◽

Vol 9 (5) ◽

pp. 713 ◽

Cited By ~ 3

Author(s):

Yeonguk Yu ◽

Yoon-Joong Kim

Keyword(s):

Emotion Recognition ◽

Motion Capture ◽

Short Term Memory ◽

Speech Emotion Recognition ◽

Main Study ◽

Short Term ◽

Attention Model ◽

Proposed Model ◽

Long Short Term Memory ◽

Spectrogram Feature

We propose a speech-emotion recognition (SER) model with an “attention-long Long Short-Term Memory (LSTM)-attention” component to combine IS09, a commonly used feature for SER, and mel spectrogram, and we analyze the reliability problem of the interactive emotional dyadic motion capture (IEMOCAP) database. The attention mechanism of the model focuses on emotion-related elements of the IS09 and mel spectrogram feature and the emotion-related duration from the time of the feature. Thus, the model extracts emotion information from a given speech signal. The proposed model for the baseline study achieved a weighted accuracy (WA) of 68% for the improvised dataset of IEMOCAP. However, the WA of the proposed model of the main study and modified models could not achieve more than 68% in the improvised dataset. This is because of the reliability limit of the IEMOCAP dataset. A more reliable dataset is required for a more accurate evaluation of the model’s performance. Therefore, in this study, we reconstructed a more reliable dataset based on the labeling results provided by IEMOCAP. The experimental results of the model for the more reliable dataset confirmed a WA of 73%.

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